1. A spam filtering method comprising employing a computer system to perform the steps of: computing a set of pattern relevancies for a set of feature patterns, wherein at least one pattern relevance of the set of pattern relevancies is computed according to a set of feature relevance weights determined through a process external to neuronal training; and classifying a target message as spam or ham according to a result of a processing of the target message by a neural network filter according to the set of pattern relevancies by: assigning a pattern relevance of the set of pattern relevancies to each neuron of a subset of neurons of the neural network filter; computing a target input vector characterizing the presence of a set of spam/ham identifying message features within the target message; selecting an active neuron of the subset of neurons according to a scalar product between the target input vector and a first set of neuronal weights of the neural network filter; computing a recognition score according to a scalar product between the target input vector and a second set of neuronal weights of the neural network filter, and according to the pattern relevance corresponding to the active neuron; and comparing the recognition score to a predefined vigilance threshold.