A new approach of classification for non-Gaussian distribution upon competitive training

In this paper, we present a new neural and statistical classification approach. This procedure uses the neural network with competitive training to detect the local maxima of the probabilities density function´s (pdf) which are considered as the prototype of the classes in the data distribution. In order to take account of different forms of distributions; Gaussian and non-Gaussian, we used as criteria of resemblance the Mahalanobis distance that takes into account the dispersion of the distributions.