diff --git a/src/structure_learning/score_based_algorithm.rs b/src/structure_learning/score_based_algorithm.rs
index b5590ed..7537483 100644
--- a/src/structure_learning/score_based_algorithm.rs
+++ b/src/structure_learning/score_based_algorithm.rs
@@ -20,6 +20,10 @@ impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
     where
         T: network::Network,
     {
+        if net.get_number_of_nodes() != dataset.get_trajectories()[0].get_events().shape()[1] {
+            panic!("Dataset and Network must have the same number of variables.")
+        }
+
         let mut net = net;
         let max_parent_set = self.max_parent_set.unwrap_or(net.get_number_of_nodes());
         net.initialize_adj_matrix();
diff --git a/tests/structure_learning.rs b/tests/structure_learning.rs
index d24170a..c3482cc 100644
--- a/tests/structure_learning.rs
+++ b/tests/structure_learning.rs
@@ -54,6 +54,54 @@ fn simple_bic() {
 
 }
 
+
+
+fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm> (sl: T) {
+    let mut net = CtbnNetwork::init();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"),3))
+        .unwrap();
+    net.add_edge(n1, n2);
+
+    match &mut net.get_node_mut(n1).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+                                  [1.5, -2.0, 0.5],
+                                  [0.4, 0.6, -1.0]]])));
+        }
+    }
+
+    match &mut net.get_node_mut(n2).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[
+                [[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]],
+                [[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]],
+                [[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]],
+            ])));
+        }
+    }
+
+    let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259),);
+
+    let mut net = CtbnNetwork::init();
+    let _n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
+        .unwrap();
+    let net = sl.fit_transform(net, &data);
+}
+
+
+#[test]
+#[should_panic]
+pub fn check_compatibility_between_dataset_and_network_hill_climbing() {
+    let ll = LogLikelihood::init(1, 1.0);
+    let hl = HillClimbing::init(ll, None);
+    check_compatibility_between_dataset_and_network(hl);
+}
+
 fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm> (sl: T) {
     let mut net = CtbnNetwork::init();
     let n1 = net