diff --git a/modules/ml/src/kdtree.cpp b/modules/ml/src/kdtree.cpp
index 1ab8400936..a80e12964a 100644
--- a/modules/ml/src/kdtree.cpp
+++ b/modules/ml/src/kdtree.cpp
@@ -101,7 +101,7 @@ medianPartition( size_t* ofs, int a, int b, const float* vals )
         int i0 = a, i1 = (a+b)/2, i2 = b;
         float v0 = vals[ofs[i0]], v1 = vals[ofs[i1]], v2 = vals[ofs[i2]];
         int ip = v0 < v1 ? (v1 < v2 ? i1 : v0 < v2 ? i2 : i0) :
-            v0 < v2 ? i0 : (v1 < v2 ? i2 : i1);
+                 v0 < v2 ? (v1 == v0 ? i2 : i0): (v1 < v2 ? i2 : i1);
         float pivot = vals[ofs[ip]];
         std::swap(ofs[ip], ofs[i2]);
 
@@ -131,7 +131,6 @@ medianPartition( size_t* ofs, int a, int b, const float* vals )
         CV_Assert(vals[ofs[k]] >= pivot);
         more += vals[ofs[k]] > pivot;
     }
-    CV_Assert(std::abs(more - less) <= 1);
 
     return vals[ofs[middle]];
 }
diff --git a/modules/ml/src/knearest.cpp b/modules/ml/src/knearest.cpp
index ca23d0f4d6..3d8f9b5d2e 100644
--- a/modules/ml/src/knearest.cpp
+++ b/modules/ml/src/knearest.cpp
@@ -381,36 +381,23 @@ public:
         Mat res, nr, d;
         if( _results.needed() )
         {
-            _results.create(testcount, 1, CV_32F);
             res = _results.getMat();
         }
         if( _neighborResponses.needed() )
         {
-            _neighborResponses.create(testcount, k, CV_32F);
             nr = _neighborResponses.getMat();
         }
         if( _dists.needed() )
         {
-            _dists.create(testcount, k, CV_32F);
             d = _dists.getMat();
         }
 
         for (int i=0; i<test_samples.rows; ++i)
         {
             Mat _res, _nr, _d;
-            if (res.rows>i)
-            {
-                _res = res.row(i);
-            }
-            if (nr.rows>i)
-            {
-                _nr = nr.row(i);
-            }
-            if (d.rows>i)
-            {
-                _d = d.row(i);
-            }
             tr.findNearest(test_samples.row(i), k, Emax, _res, _nr, _d, noArray());
+            res.push_back(_res.t());
+            _results.assign(res);
         }
 
         return result; // currently always 0
diff --git a/modules/ml/test/test_knearest.cpp b/modules/ml/test/test_knearest.cpp
index 49e6b0d12a..80baed9626 100644
--- a/modules/ml/test/test_knearest.cpp
+++ b/modules/ml/test/test_knearest.cpp
@@ -37,18 +37,31 @@ TEST(ML_KNearest, accuracy)
         EXPECT_LE(err, 0.01f);
     }
     {
-        // TODO: broken
-#if 0
         SCOPED_TRACE("KDTree");
-        Mat bestLabels;
+        Mat neighborIndexes;
         float err = 1000;
         Ptr<KNearest> knn = KNearest::create();
         knn->setAlgorithmType(KNearest::KDTREE);
         knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
-        knn->findNearest(testData, 4, bestLabels);
+        knn->findNearest(testData, 4, neighborIndexes);
+        Mat bestLabels;
+        // The output of the KDTree are the neighbor indexes, not actual class labels
+        // so we need to do some extra work to get actual predictions
+        for(int row_num = 0; row_num < neighborIndexes.rows; ++row_num){
+            vector<float> labels;
+            for(int index = 0; index < neighborIndexes.row(row_num).cols; ++index) {
+                labels.push_back(trainLabels.at<float>(neighborIndexes.row(row_num).at<int>(0, index) , 0));
+            }
+            // computing the mode of the output class predictions to determine overall prediction
+            std::vector<int> histogram(3,0);
+            for( int i=0; i<3; ++i )
+                ++histogram[ static_cast<int>(labels[i]) ];
+            int bestLabel = static_cast<int>(std::max_element( histogram.begin(), histogram.end() ) - histogram.begin());
+            bestLabels.push_back(bestLabel);
+        }
+        bestLabels.convertTo(bestLabels, testLabels.type());
         EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
         EXPECT_LE(err, 0.01f);
-#endif
     }
 }
 
@@ -74,4 +87,26 @@ TEST(ML_KNearest, regression_12347)
     EXPECT_EQ(2, zBestLabels.at<float>(1,0));
 }
 
+TEST(ML_KNearest, bug_11877)
+{
+    Mat trainData = (Mat_<float>(5,2) << 3, 3, 3, 3, 4, 4, 4, 4, 4, 4);
+    Mat trainLabels = (Mat_<float>(5,1) << 0, 0, 1, 1, 1);
+
+    Ptr<KNearest> knnKdt = KNearest::create();
+    knnKdt->setAlgorithmType(KNearest::KDTREE);
+    knnKdt->setIsClassifier(true);
+
+    knnKdt->train(trainData, ml::ROW_SAMPLE, trainLabels);
+
+    Mat testData = (Mat_<float>(2,2) << 3.1, 3.1, 4, 4.1);
+    Mat testLabels = (Mat_<int>(2,1) << 0, 1);
+    Mat result;
+
+    knnKdt->findNearest(testData, 1, result);
+
+    EXPECT_EQ(1, int(result.at<int>(0, 0)));
+    EXPECT_EQ(2, int(result.at<int>(1, 0)));
+    EXPECT_EQ(0, trainLabels.at<int>(result.at<int>(0, 0), 0));
+}
+
 }} // namespace