diff --git a/pyerrors/correlators.py b/pyerrors/correlators.py
index 95eadf74..736bcb7a 100644
--- a/pyerrors/correlators.py
+++ b/pyerrors/correlators.py
@@ -69,9 +69,6 @@ class Corr:
 
         if isinstance(data_input, list):
 
-            if all([isinstance(item, (Obs, CObs)) for item in data_input]):
-                _assert_equal_properties(data_input)
-                self.content = [np.asarray([item]) for item in data_input]
             if all([isinstance(item, (Obs, CObs)) or item is None for item in data_input]):
                 _assert_equal_properties([o for o in data_input if o is not None])
                 self.content = [np.asarray([item]) if item is not None else None for item in data_input]
@@ -972,6 +969,8 @@ class Corr:
             content_string += "Description: " + self.tag + "\n"
         if self.N != 1:
             return content_string
+        if isinstance(self[0], CObs):
+            return content_string
 
         if print_range[1]:
             print_range[1] += 1
@@ -1139,8 +1138,10 @@ class Corr:
         for t in range(self.T):
             if _check_for_none(self, newcontent[t]):
                 continue
-            if np.isnan(np.sum(newcontent[t]).value):
-                newcontent[t] = None
+            tmp_sum = np.sum(newcontent[t])
+            if hasattr(tmp_sum, "value"):
+                if np.isnan(tmp_sum.value):
+                    newcontent[t] = None
         if all([item is None for item in newcontent]):
             raise Exception('Operation returns undefined correlator')
         return Corr(newcontent)
@@ -1197,8 +1198,8 @@ class Corr:
     @property
     def real(self):
         def return_real(obs_OR_cobs):
-            if isinstance(obs_OR_cobs, CObs):
-                return obs_OR_cobs.real
+            if isinstance(obs_OR_cobs.flatten()[0], CObs):
+                return np.vectorize(lambda x: x.real)(obs_OR_cobs)
             else:
                 return obs_OR_cobs
 
@@ -1207,8 +1208,8 @@ class Corr:
     @property
     def imag(self):
         def return_imag(obs_OR_cobs):
-            if isinstance(obs_OR_cobs, CObs):
-                return obs_OR_cobs.imag
+            if isinstance(obs_OR_cobs.flatten()[0], CObs):
+                return np.vectorize(lambda x: x.imag)(obs_OR_cobs)
             else:
                 return obs_OR_cobs * 0  # So it stays the right type
 
diff --git a/pyerrors/misc.py b/pyerrors/misc.py
index bbfb8e7d..cf0dd18a 100644
--- a/pyerrors/misc.py
+++ b/pyerrors/misc.py
@@ -105,16 +105,11 @@ def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
 
 
 def _assert_equal_properties(ol, otype=Obs):
-    if not isinstance(ol[0], otype):
-        raise Exception("Wrong data type in list.")
+    otype = type(ol[0])
     for o in ol[1:]:
         if not isinstance(o, otype):
             raise Exception("Wrong data type in list.")
-        if not ol[0].is_merged == o.is_merged:
-            raise Exception("All Obs in list have to have the same state 'is_merged'.")
-        if not ol[0].reweighted == o.reweighted:
-            raise Exception("All Obs in list have to have the same property 'reweighted'.")
-        if not ol[0].e_content == o.e_content:
-            raise Exception("All Obs in list have to be defined on the same set of configs.")
-        if not ol[0].idl == o.idl:
-            raise Exception("All Obs in list have to be defined on the same set of configurations.")
+        for attr in ["is_merged", "reweighted", "e_content", "idl"]:
+            if hasattr(ol[0], attr):
+                if not getattr(ol[0], attr) == getattr(o, attr):
+                    raise Exception(f"All Obs in list have to have the same state '{attr}'.")
diff --git a/tests/correlators_test.py b/tests/correlators_test.py
index 49332254..1ab31f12 100644
--- a/tests/correlators_test.py
+++ b/tests/correlators_test.py
@@ -532,3 +532,13 @@ def test_prune():
     with pytest.raises(Exception):
         corr_mat.prune(3)
         corr_mat.prune(4)
+
+
+def test_complex_Corr():
+    o1 = pe.pseudo_Obs(1.0, 0.1, "test")
+    cobs = pe.CObs(o1, -o1)
+    ccorr = pe.Corr([cobs, cobs, cobs])
+    assert np.all([ccorr.imag[i] == -ccorr.real[i] for i in range(ccorr.T)])
+    print(ccorr)
+    mcorr = pe.Corr(np.array([[ccorr, ccorr], [ccorr, ccorr]]))
+    assert np.all([mcorr.imag[i] == -mcorr.real[i] for i in range(mcorr.T)])