1. A method of pattern classification using a probabilistic neural network, comprising the steps of: comparing a set of pattern vectors contained in an initial hidden layer of an initial probabilistic neural network to a set of neurons in a hidden layer of a probabilistic neural network obtained after processing said pattern vectors using a learning vector quantization procedure based on an Euclidian distance; identifying which neurons of said set of neurons are the nearest to each pattern vector of said set of pattern vectors; and eliminating from said probabilistic neural network those neurons of said set of neurons which have not been identified as being nearest to any of said set of pattern vectors.