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Artificial neural networks (ANN) have demonstrated good predictive performance in a wide variety of real world problems. However, there are strong arguments as to why ANNs are insufficient for data mining. The arguments are the poor comprehensibility of the learned ANNs, which is the inability to represent the learned knowledge in an understandable way to the users. In this paper, Artificial Neural Network Tree (ANNT), ie ANN training preceded by Decision Tree rules extraction method, is presented to overcome the comprehensibility problem of ANN. Experimental results on three data sets show that the proposed algorithm generates rules that are better than C4. 5. This paper provides an evaluation of the proposed method in terms of accuracy, comprehensibility and fidelity.