The invention relates to n-tuple or RAM based neural network classification methods and systems and, more particularly, to n-tuple or RAM based classification systems where the decision criteria applied to obtain the output sources and compare these output sources to obtain a classification are determined during a training process. Accordingly, the invention relates to a system and a method of training a computer classification system which can be defined by a network comprising a number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of possible classes and comprising columns being addressed by signals or elements of sampled training input data examples.