This dissertation proposes a methodology, established in quantitative procedures, to assist the diagnostic of individuals with heart disease. The proposed methodology was implemented and analyzed in a group of individuals of the public database called Heart Disease Database (Aha, current in 2001), diagnosed in the cities of Cleveland and Long Beach, in the United States. The results gotten in this study had been compared with the results of other authors found in literature to have a measure of the quality of the results gotten here. Others techniques of classification of standards known in literature had also been used, called Discriminate Analysis and C4.5 Algorithm, to establish comparisons with the results gotten in this dissertation using Neural Networks, and to apply the methodology suggested in the division of the sets of training/generalization. The gotten results were satisfactory. A percentage of average rightness of 91.0 % was reached, whereas other results of studies using the same database had reached percentages of average rightness of 83.0 % (Ho & Chou, 2001) and 83.5 % (Hu, Li, Cai & Xu, 2004). The performance of the Neural Network was also better when compared with Discriminate Analysis and C4.5 Algorithm. The methodology of division of the sets of training/generalization suggested in this dissertation promoted improvements in all the three used techniques of classification of standards. It´s believable that the gotten results will be able to assist the medical behaviors in relation to the diagnostic of heart disease, becoming useful in the prevention and/or treatment of heart diseases.