diff --git a/gtsam/hybrid/tests/testHybridSmoother.cpp b/gtsam/hybrid/tests/testHybridSmoother.cpp
index 5493ce53e..815e32560 100644
--- a/gtsam/hybrid/tests/testHybridSmoother.cpp
+++ b/gtsam/hybrid/tests/testHybridSmoother.cpp
@@ -167,6 +167,61 @@ TEST(HybridSmoother, ValidPruningError) {
   EXPECT_DOUBLES_EQUAL(1e-8, errorTree(delta.discrete()), 1e-8);
 }
 
+/****************************************************************************/
+// Test if dead mode removal works.
+TEST(HybridSmoother, DeadModeRemoval) {
+  using namespace estimation_fixture;
+
+  size_t K = 8;
+
+  // Switching example of robot moving in 1D
+  // with given measurements and equal mode priors.
+  HybridNonlinearFactorGraph graph;
+  Values initial;
+  Switching switching = InitializeEstimationProblem(
+      K, 0.1, 0.1, measurements, "1/1 1/1", &graph, &initial);
+
+  // Smoother with dead mode removal enabled.
+  HybridSmoother smoother(true);
+
+  constexpr size_t maxNrLeaves = 3;
+  for (size_t k = 1; k < K; k++) {
+    if (k > 1) graph.push_back(switching.modeChain.at(k - 1));  // Mode chain
+    graph.push_back(switching.binaryFactors.at(k - 1));         // Motion Model
+    graph.push_back(switching.unaryFactors.at(k));              // Measurement
+
+    initial.insert(X(k), switching.linearizationPoint.at<double>(X(k)));
+
+    HybridGaussianFactorGraph linearized = *graph.linearize(initial);
+
+    // std::cout << "\n\n\nk" << std::endl;
+    // GTSAM_PRINT(linearized);
+    smoother.update(linearized, maxNrLeaves);
+
+    // Clear all the factors from the graph
+    graph.resize(0);
+  }
+
+  // Get the continuous delta update as well as
+  // the optimal discrete assignment.
+  HybridValues delta = smoother.hybridBayesNet().optimize();
+
+  // Check discrete assignment
+  DiscreteValues expected_discrete;
+  for (size_t k = 0; k < K - 1; k++) {
+    expected_discrete[M(k)] = discrete_seq[k];
+  }
+  EXPECT(assert_equal(expected_discrete, delta.discrete()));
+
+  // Update nonlinear solution and verify
+  Values result = initial.retract(delta.continuous());
+  Values expected_continuous;
+  for (size_t k = 0; k < K; k++) {
+    expected_continuous.insert(X(k), measurements[k]);
+  }
+  EXPECT(assert_equal(expected_continuous, result));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;