diff --git a/paddle/parameter/FirstOrderOptimizer.cpp b/paddle/parameter/FirstOrderOptimizer.cpp
index dbb738e98b5874f5bb33026ad585a6c3ef327d1d..5938b2210c7174c9a0ce659220825b74af007db5 100644
--- a/paddle/parameter/FirstOrderOptimizer.cpp
+++ b/paddle/parameter/FirstOrderOptimizer.cpp
@@ -161,6 +161,7 @@ void AdaDeltaParameterOptimizer::update(const VectorPtr vecs[],
                                         const ParameterConfig& config,
                                         size_t sparseId) const {
   CHECK(sparseId == -1LU) << "Sparse update is not supported";
+
   BaseMatrix& value = *vecs[PARAMETER_VALUE];
   BaseMatrix& grad = *vecs[PARAMETER_GRADIENT];
   BaseMatrix& mom = *vecs[PARAMETER_MOMENTUM];
@@ -265,6 +266,7 @@ void AdamParameterOptimizer::update(const VectorPtr vecs[],
                                     const ParameterConfig& config,
                                     size_t sparseId) const {
   CHECK(sparseId == -1UL) << "Sparse update is not supported";
+
   real beta1_power = std::pow(beta1_, step_);
   real beta2_power = std::pow(beta2_, step_);
   real learningRate = config.learning_rate() * learningRate_;
@@ -303,18 +305,25 @@ void AdamaxParameterOptimizer::update(const VectorPtr vecs[],
 void OptimizerWithGradientClipping::update(const VectorPtr vecs[],
                                            const ParameterConfig& config,
                                            size_t sparseId) const {
+  real globalThreshold = optConfig_.gradient_clipping_threshold();
+  real localThreshold = config.gradient_clipping_threshold();
+
+  // Use local gradient clipping threshold if it's enabled,
+  // otherwise using the global one.
+  real threshold = localThreshold > 0.0f ? localThreshold : globalThreshold;
+  std::string field = localThreshold > 0.0f ? "local" : "global";
+
   real maxAbsGrad = vecs[PARAMETER_GRADIENT]->getAbsMax();
-  if (maxAbsGrad > config.gradient_clipping_threshold()) {
+  if (maxAbsGrad > threshold) {
     if (FLAGS_log_clipping) {
       real avgAbsGrad = vecs[PARAMETER_GRADIENT]->getAbsSum() /
                         vecs[PARAMETER_GRADIENT]->getSize();
-      LOG(INFO) << "parameter=" << config.name() << " need clipping,"
-                << " max grad=" << maxAbsGrad << " avg grad=" << avgAbsGrad;
+      LOG(INFO) << "parameter=" << config.name() << " need clipping by "
+                << field << " threshold=" << threshold
+                << ", max grad=" << maxAbsGrad << ", avg grad=" << avgAbsGrad;
     }
-    vecs[PARAMETER_GRADIENT]->clip(-config.gradient_clipping_threshold(),
-                                   config.gradient_clipping_threshold());
+    vecs[PARAMETER_GRADIENT]->clip(-threshold, threshold);
   }
-
   optimizer_->update(vecs, config, sparseId);
 }
 
diff --git a/paddle/parameter/OptimizerWithRegularizer.cpp b/paddle/parameter/OptimizerWithRegularizer.cpp
index 85f13c8bc08c534224a1a8365d541737980b439f..7910b12444938a0555c211bb3dfd0f4209e480ec 100644
--- a/paddle/parameter/OptimizerWithRegularizer.cpp
+++ b/paddle/parameter/OptimizerWithRegularizer.cpp
@@ -131,7 +131,8 @@ ParameterOptimizer* OptimizerWithRegularizer::create(
     bool inPserver) {
   ParameterOptimizer* optimizer =
       ParameterOptimizer::create(optConfig, inPserver);
-  if (paraConfig.gradient_clipping_threshold() > 0.0f &&
+  if ((optConfig.gradient_clipping_threshold() > 0.0f ||
+       paraConfig.gradient_clipping_threshold() > 0.0f) &&
       !dynamic_cast<AddOptimizer*>(optimizer)) {
     optimizer = new OptimizerWithGradientClipping(optConfig, optimizer);
   }
diff --git a/paddle/parameter/ParameterOptimizer.h b/paddle/parameter/ParameterOptimizer.h
index 2bdc793d605e01f8e055087bb3e0973168cb0213..f98ba569b569379b30d034739a7f84aaf97108db 100644
--- a/paddle/parameter/ParameterOptimizer.h
+++ b/paddle/parameter/ParameterOptimizer.h
@@ -167,6 +167,7 @@ public:
     }
     parameterTypes_.push_back(type);
   }
+
   real getLearningRate() const { return learningRate_; }
 
   virtual void setNoDecay() { applyDecay_ = false; }
@@ -201,6 +202,7 @@ protected:
    * so, if lr change in StartBatch, please assign to learningRate_
    */
   real learningRate_;
+
   std::unique_ptr<LearningRateScheduler> learningRateScheduler_;
   int64_t pass_;  // current training pass (starting from 0)
   bool firstTime_;
diff --git a/proto/TrainerConfig.proto b/proto/TrainerConfig.proto
index a334e07b6282a6ff9867482e0c3a299df2a78d1d..a819d20d11ff3932d331801007b8cfb9c77a3f2b 100644
--- a/proto/TrainerConfig.proto
+++ b/proto/TrainerConfig.proto
@@ -128,6 +128,9 @@ message OptimizationConfig {
   // when async_lagged_grad_discard_ratio * num_gradient_servers commit passed,
   // current async gradient will be discard silently.
   optional double async_lagged_grad_discard_ratio = 37 [default = 1.5];
+
+  // global threshold for gradient clipping 
+  optional double gradient_clipping_threshold = 38 [default = 0.0];
 };
 
 message TrainerConfig {
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 9135f38719a44e3070f42e478d0fc6b0004227b5..9fe8794691e5f742b3c290850d7f2f4db4862cf4 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -3377,6 +3377,7 @@ settings = dict(
     algorithm='async_sgd',
     async_lagged_grad_discard_ratio=1.5,
     learning_method='momentum',
+    gradient_clipping_threshold=None,
     num_batches_per_send_parameter=None,
     num_batches_per_get_parameter=None,
     center_parameter_update_method=None,
diff --git a/python/paddle/trainer_config_helpers/optimizers.py b/python/paddle/trainer_config_helpers/optimizers.py
index a53ebe160be3b5d6d115e3e15d059d3d87e80942..c3495ee110bfaf91a47637a52e88b3bb56dce7a9 100644
--- a/python/paddle/trainer_config_helpers/optimizers.py
+++ b/python/paddle/trainer_config_helpers/optimizers.py
@@ -408,7 +408,8 @@ def settings(batch_size,
 
     args = [
         'batch_size', 'learning_rate', 'learning_rate_decay_a',
-        'learning_rate_decay_b', 'learning_rate_schedule', 'learning_rate_args'
+        'learning_rate_decay_b', 'learning_rate_schedule', 'learning_rate_args',
+        'gradient_clipping_threshold'
     ]
     kwargs = dict()
     kwargs['algorithm'] = algorithm