diff --git a/samples/cpp/letter_recog.cpp b/samples/cpp/letter_recog.cpp
index 4076b639f75b2f403867f6d14f6508b9c67d04ce..b6a35e338f74fdc552062c5270f4e7c21748132c 100644
--- a/samples/cpp/letter_recog.cpp
+++ b/samples/cpp/letter_recog.cpp
@@ -129,7 +129,7 @@ static void test_and_save_classifier(const Ptr<StatModel>& model,
         Mat sample = data.row(i);
 
         float r = model->predict( sample );
-        r = std::abs(r + rdelta - responses.at<int>(i)) <= FLT_EPSILON ? 1 : 0;
+        r = std::abs(r + rdelta - responses.at<int>(i)) <= FLT_EPSILON ? 1.f : 0.f;
 
         if( i < ntrain_samples )
             train_hr += r;
diff --git a/samples/cpp/points_classifier.cpp b/samples/cpp/points_classifier.cpp
index 3aa4d9b1379da92c14160a6b05afa4382a940d35..eedec4b6a898e17f23adcf2fa0aeb174b497674a 100644
--- a/samples/cpp/points_classifier.cpp
+++ b/samples/cpp/points_classifier.cpp
@@ -208,7 +208,7 @@ static void find_decision_boundary_ANN( const Mat&  layer_sizes )
     ANN_MLP::Params params(layer_sizes, ANN_MLP::SIGMOID_SYM, 1, 1, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON),
                            ANN_MLP::Params::BACKPROP, 0.001);
 
-    Mat trainClasses = Mat::zeros( trainedPoints.size(), classColors.size(), CV_32FC1 );
+    Mat trainClasses = Mat::zeros( (int)trainedPoints.size(), (int)classColors.size(), CV_32FC1 );
     for( int i = 0; i < trainClasses.rows; i++ )
     {
         trainClasses.at<float>(i, trainedPointsMarkers[i]) = 1.f;
@@ -386,7 +386,7 @@ int main()
             Mat layer_sizes1( 1, 3, CV_32SC1 );
             layer_sizes1.at<int>(0) = 2;
             layer_sizes1.at<int>(1) = 5;
-            layer_sizes1.at<int>(2) = classColors.size();
+            layer_sizes1.at<int>(2) = (int)classColors.size();
             find_decision_boundary_ANN( layer_sizes1 );
             imshow( "ANN", imgDst );
 #endif