1. A method for selectively classifying at least one input pattern into a respective one of a plurality of pattern classes, each input pattern being characterized by at least first and second input values represented by respective first and second electrical input signals having magnitudes that correspond to first and second features of the respective input pattern, each pattern class being characterized by at least first and second feature classification criteria represented by respective electrical feature classification signals having magnitudes that correspond to first and second features of the respective pattern class, comprising the steps of: A) defining, for each of said plurality of pattern classes, said first and second feature classification criteria as respective first and second fuzzy windows, each of said windows having respectively a maximum, a minimum and an optimum electrical feature classification signal value; B) for each of said plurality of pattern classes: 1) determining if the first input value of said input pattern is within the first fuzzy window of said pattern class; and, a) if so, providing a first similarity metric signal having a value which varies from a predetermined high significance value when the first input value corresponds to the optimum feature classification signal value of the first fuzzy window of said pattern class to a predetermined low significance signal value when the first input value corresponds to either the maximum or minimum feature classification signal value of the first fuzzy window of said pattern class; but b) if not, assigning the first similarity metric signal a predetermined no significance value, 2) determining if the second input value of said input pattern is within the second fuzzy window of said pattern class, and a) if so, providing a second similarity metric signal value having a value which varies from a predetermined high significance value when the second input value corresponds to the optimum feature classification signal value of the second fuzzy window of said pattern class to a low significance value when the second input value corresponds to either the maximum or minimum feature classification signal value of the second fuzzy window of said pattern class; but b) if not, assigning the second similarity metric signal a predetermined no significance value, C) generating level metric signals in response to the similarity metric signals produced by the input values and the respective fuzzy windows of each pattern class; D) comparing the level metric signals for each of said pattern classes; and E) generating a class index signal indicative of the one of said pattern classes which has the most significant level metric signal.