diff --git a/sklearn/ensemble/tests/test_voting_classifier.py b/sklearn/ensemble/tests/test_voting_classifier.py
index 4765d0e32d0bb..023be79912d12 100644
--- a/sklearn/ensemble/tests/test_voting_classifier.py
+++ b/sklearn/ensemble/tests/test_voting_classifier.py
@@ -17,6 +17,7 @@
 from sklearn.svm import SVC
 from sklearn.multiclass import OneVsRestClassifier
 from sklearn.neighbors import KNeighborsClassifier
+from sklearn.base import BaseEstimator, ClassifierMixin
 
 
 # Load the iris dataset and randomly permute it
@@ -274,6 +275,20 @@ def test_sample_weight():
     assert_raise_message(ValueError, msg, eclf3.fit, X, y, sample_weight)
 
 
+def test_sample_weight_kwargs():
+    """Check that VotingClassifier passes sample_weight as kwargs"""
+    class MockClassifier(BaseEstimator, ClassifierMixin):
+        """Mock Classifier to check that sample_weight is received as kwargs"""
+        def fit(self, X, y, *args, **sample_weight):
+            assert_true('sample_weight' in sample_weight)
+
+    clf = MockClassifier()
+    eclf = VotingClassifier(estimators=[('mock', clf)], voting='soft')
+
+    # Should not raise an error.
+    eclf.fit(X, y, sample_weight=np.ones((len(y),)))
+
+
 def test_set_params():
     """set_params should be able to set estimators"""
     clf1 = LogisticRegression(random_state=123, C=1.0)
diff --git a/sklearn/ensemble/voting_classifier.py b/sklearn/ensemble/voting_classifier.py
index 88b329d836978..ad6c0125dd664 100644
--- a/sklearn/ensemble/voting_classifier.py
+++ b/sklearn/ensemble/voting_classifier.py
@@ -23,10 +23,10 @@
 from ..utils.metaestimators import _BaseComposition
 
 
-def _parallel_fit_estimator(estimator, X, y, sample_weight):
+def _parallel_fit_estimator(estimator, X, y, sample_weight=None):
     """Private function used to fit an estimator within a job."""
     if sample_weight is not None:
-        estimator.fit(X, y, sample_weight)
+        estimator.fit(X, y, sample_weight=sample_weight)
     else:
         estimator.fit(X, y)
     return estimator
@@ -185,7 +185,7 @@ def fit(self, X, y, sample_weight=None):
 
         self.estimators_ = Parallel(n_jobs=self.n_jobs)(
                 delayed(_parallel_fit_estimator)(clone(clf), X, transformed_y,
-                                                 sample_weight)
+                                                 sample_weight=sample_weight)
                 for clf in clfs if clf is not None)
 
         return self