diff --git a/Orange/widgets/evaluate/owconfusionmatrix.py b/Orange/widgets/evaluate/owconfusionmatrix.py
index ac21708be6b..d23592df1a1 100644
--- a/Orange/widgets/evaluate/owconfusionmatrix.py
+++ b/Orange/widgets/evaluate/owconfusionmatrix.py
@@ -250,7 +250,6 @@ def set_results(self, results):
 
             self._init_table(len(class_values))
             self.openContext(data.domain.class_var)
-            print("ZZZ", prev_sel_learner)
             if not prev_sel_learner or prev_sel_learner[0] >= len(self.learners):
                 self.selected_learner[:] = [0]
             else:
diff --git a/Orange/widgets/evaluate/tests/__init__.py b/Orange/widgets/evaluate/tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/Orange/widgets/evaluate/tests/test_owconfusionmatrix.py b/Orange/widgets/evaluate/tests/test_owconfusionmatrix.py
new file mode 100644
index 00000000000..cfe0c79fea4
--- /dev/null
+++ b/Orange/widgets/evaluate/tests/test_owconfusionmatrix.py
@@ -0,0 +1,41 @@
+# pylint: disable=missing-docstring
+
+from Orange.data import Table
+from Orange.classification import NaiveBayesLearner, TreeLearner
+from Orange.evaluation.testing import CrossValidation
+from Orange.widgets.evaluate.owconfusionmatrix import OWConfusionMatrix
+from Orange.widgets.tests.base import WidgetTest
+
+class TestOWClassificationTree(WidgetTest):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        bayes = NaiveBayesLearner()
+        tree = TreeLearner()
+        iris = Table("iris")
+        titanic = Table("titanic")
+        common = dict(k=3, store_data=True)
+        cls.results_1_iris = CrossValidation(iris, [bayes], **common)
+        cls.results_2_iris = CrossValidation(iris, [bayes, tree], **common)
+        cls.results_2_titanic = CrossValidation(titanic, [bayes, tree],
+                                                **common)
+
+    def setUp(self):
+        self.widget = self.create_widget(OWConfusionMatrix,
+                                         stored_settings={"auto_apply": False})
+
+    def test_selected_learner(self):
+        """Check learner and model for various values of all parameters
+        when pruning parameters are not checked
+        """
+        self.widget.set_results(self.results_2_iris)
+        self.assertEqual(self.widget.selected_learner, [0])
+        self.widget.selected_learner[:] = [1]
+        self.widget.set_results(self.results_2_titanic)
+        self.widget.selected_learner[:] = [1]
+        self.widget.set_results(self.results_1_iris)
+        self.widget.selected_learner[:] = [0]
+        self.widget.set_results(None)
+        self.widget.set_results(self.results_1_iris)
+        self.widget.selected_learner[:] = [0]
+