Rules are extracted from the DIMLP neural network in polynomial time with respect to the size of the classification problem and the size of the network. With rules is possible to ask how well do inferences made compare with knowledge and heuristics of experts. Although fidelity of generated rules from the training set is 100%, perfect fidelity on new unknown data samples is not guaranteed. In this work we introduce a local dynamic algorithm that makes rules consistent with new unknown cases. The presented method is computationally tractable and produces small changes in a rulebase.