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Neural Network is a powerful pattern recognition algorithm capable of learning complex non-linear patterns. However, Neural Networks have a well-known drawback of being a “Black Box” learner that is not comprehensible or transferable thus making it unsuitable tasks that require a rational justification for making a decision. Rule Extraction methods can resolve this limitation by extracting comprehensible rules from a trained Network. In this paper, we present an algorithm called HERETIC that uses a symbolic learning algorithm (Decision Tree) on each unit of the Neural Network. Experiments and theoretical analysis show HERETIC generates highly accurate rules that closely approximates the Neural Network.