diff --git a/python/tests/test_haplotype_matching.py b/python/tests/test_haplotype_matching.py
index bdcde5fad3..43f84a1503 100644
--- a/python/tests/test_haplotype_matching.py
+++ b/python/tests/test_haplotype_matching.py
@@ -1022,19 +1022,19 @@ def verify(self, ts):
 # TODO add params to run the various checks
 def check_viterbi(ts, h, recombination=None, mutation=None):
     h = np.array(h).astype(np.int8)
-    n = ts.num_samples
-    assert len(h) == ts.num_sites
+    m = ts.num_sites
+    assert len(h) == m
     if recombination is None:
         recombination = np.zeros(ts.num_sites) + 1e-9
     if mutation is None:
         mutation = np.zeros(ts.num_sites)
     precision = 22
 
-    H = ts.genotype_matrix().T
+    G = ts.genotype_matrix()
 
     path, ll = ls.viterbi(
-        H,
-        h.reshape(1, n),
+        G,
+        h.reshape(1, m),
         recombination,
         mutation_rate=mutation,
         scale_mutation_based_on_n_alleles=False,
@@ -1050,8 +1050,8 @@ def check_viterbi(ts, h, recombination=None, mutation=None):
     # the same as ll_tree (and ll).
     path_tree = cm.traceback()
     ll_check = ls.path_ll(
-        H,
-        h.reshape(1, n),
+        G,
+        h.reshape(1, m),
         path_tree,
         recombination,
         mutation_rate=mutation,
@@ -1079,16 +1079,16 @@ def check_forward_matrix(ts, h, recombination=None, mutation=None):
     h = np.array(h).astype(np.int8)
     n = ts.num_samples
     m = ts.num_sites
-    assert len(h) == ts.num_sites
+    assert len(h) == m
     if recombination is None:
         recombination = np.zeros(ts.num_sites) + 1e-9
     if mutation is None:
         mutation = np.zeros(ts.num_sites)
 
-    H = ts.genotype_matrix().T
+    G = ts.genotype_matrix()
     F, c, ll = ls.forwards(
-        H,
-        h.reshape(1, n),
+        G,
+        h.reshape(1, m),
         recombination,
         mutation_rate=mutation,
         scale_mutation_based_on_n_alleles=False,
@@ -1120,17 +1120,17 @@ def check_forward_matrix(ts, h, recombination=None, mutation=None):
 def check_backward_matrix(ts, h, forward_cm, recombination=None, mutation=None):
     precision = 22
     h = np.array(h).astype(np.int8)
-    n = ts.num_samples
-    assert len(h) == ts.num_sites
+    m = ts.num_sites
+    assert len(h) == m
     if recombination is None:
         recombination = np.zeros(ts.num_sites) + 1e-9
     if mutation is None:
         mutation = np.zeros(ts.num_sites)
 
-    H = ts.genotype_matrix().T
+    G = ts.genotype_matrix()
     B = ls.backwards(
-        H,
-        h.reshape(1, n),
+        G,
+        h.reshape(1, m),
         forward_cm.normalisation_factor,
         recombination,
         mutation_rate=mutation,
@@ -1148,8 +1148,6 @@ def check_backward_matrix(ts, h, forward_cm, recombination=None, mutation=None):
     nt.assert_array_equal(
         backward_cm.normalisation_factor, forward_cm.normalisation_factor
     )
-    B_tree = backward_cm.decode()
-    nt.assert_array_almost_equal(B, B_tree)
 
     ll_ts = ts._ll_tree_sequence
     ls_hmm = _tskit.LsHmm(ll_ts, recombination, mutation, precision=precision)
@@ -1170,11 +1168,17 @@ def check_backward_matrix(ts, h, forward_cm, recombination=None, mutation=None):
         for node in py_site.keys():
             assert py_site[node] == lib_site[node]
 
+    # Something weird is happening here - why don't these agree?
+
     nt.assert_array_equal(cm_lib.normalisation_factor, forward_cm.normalisation_factor)
     B_lib = cm_lib.decode()
+    B_tree = backward_cm.decode()
+    # print(B_tree)
     # print(B_lib)
-    # print(B)
+    nt.assert_array_almost_equal(B_tree, B_lib)
     nt.assert_array_almost_equal(B, B_lib)
+    # print(B_lib)
+    # print(B)
 
 
 def add_unique_sample_mutations(ts, start=0):
@@ -1258,3 +1262,16 @@ def test_switch_each_sample_missing_middle(self):
         nt.assert_array_equal([0, 3, 3, 3], path)
         cm = check_forward_matrix(ts, h)
         check_backward_matrix(ts, h, cm)
+
+
+class TestSimulationExamples:
+    @pytest.mark.parametrize("n", [5, 10, 50])
+    @pytest.mark.parametrize("L", [1, 10, 100])
+    def test_continuous_genome(self, n, L):
+        ts = msprime.simulate(
+            n, length=L, recombination_rate=1, mutation_rate=1, random_seed=42
+        )
+        h = np.zeros(ts.num_sites, dtype=np.int8)
+        check_viterbi(ts, h)
+        cm = check_forward_matrix(ts, h)
+        check_backward_matrix(ts, h, cm)