Making diagnosis by learning from examples is a typical field of artificial neural networks. However, justifications of network responses are difficult to obtain, especially when input examples have analog variables. We propose a particular multi-layer Perceptron model in which explanations of responses are obtained through symbolic rules. The originality of this model consists in its architecture. Experiments using three datasets related to breast cancer diagnosis, coronary heart disease and thyroid dysfunctions have shown high mean predictive accuracy (respectively: 96.3%, 90.0%, 99.3%). Comparisons with the C4.5 algorithm, which builds inductive decision trees, have shown that the predictive accuracy of both approaches is roughly the same, with neural networks slightly more accurate.