1. A method for processing data representing an image of a fingerprint, comprising the steps of: prescreening the data to determine locations of regions of interest in the fingerprint data; for a determined location of a region of interest, extracting a set of feature vectors within an area that includes the determined location; applying the extracted set of feature vectors to a plurality of input nodes of a multi-layer neural network, the plurality of input nodes comprising an input layer for inputting the extracted set of feature vectors, the multi-layer neural network further comprising a multi-node output layer, and at least one multi-node hidden layer, the multi-layer neural network operating in accordance with a set of weighting functions that are mapped onto said multi-layer neural network for weighting inputs to the plurality of input nodes, for weighting inputs to the nodes of the at least one hidden layer, and for weighting inputs to the nodes of the output layer; from an output of the multi-layer neural network, determining a set of probabilities that the set of feature vectors represent individual ones of a plurality of predetermined local pattern types; and determining in accordance with a rule based classification technique, from most probable ones of the local pattern types for the identified regions of interest, and also from locations of the local pattern types within the image of the fingerprint, a most probable fingerprint type that the fingerprint data represents; wherein the step of applying includes a preliminary step of predetermining values of individual ones of the set of weights in accordance with a supervised, Bayesian model-based training sequence wherein features indicative of a predetermined set of fingerprint local pattern types are clustered within a feature space, wherein the multi-layer neural network operates in accordance with the expression: ##EQU11## wherein the step of determining a set of probabilities operates in accordance with the expression: ##EQU12## in which expressions P.sub.rNN (O.sub.k /f) is the Bayesian a posteriori probability estimate for object O.sub.k ; a.sub.k is the a priori probability; F.sub.1, F.sub.2, F.sub.3 are the saturated forms of F.sub.1 (x)=x, F.sub.2 (x)=e.sup.x, and F.sub.3 (x)=0 if x &lt;0 and F.sub.3 (x)=x if x .gtoreq.0; b.sub.j.sup.k is the subgroup weightings; c.sub.i.sup.JK, m.sub.i.sup.JK, and .sigma..sub.i.sup.JK are respectively the relative weightings of the features f.sub.i, the mean, and the standard deviation of the feature training values for feature f.sub.i in subgroup j of the k-th object, and in which F.sub.1, m.sub.i.sup.JK and .sigma..sub.i.sup.JK are associated with the input layer of the neural network, F.sub.2 and c.sub.i.sup.JK are associated with the at least one hidden layer of the neural network and F.sub.3 and b.sub.j.sup.K are associated with the output layer of the neural network; and wherein the function that applies weights to the plurality of input nodes of the multi-layer neural network is given by the expression: ##EQU13##