1. A method for processing a set of at least two training signatures provided by a person, leading to a stored model of the class of signatures made by said person, the method comprising, for each said signature: receiving a digitally sampled signature signal and storing it as a raw signature; smoothing and normalizing the raw signature and storing the result as a preprocessed signature; dividing the pre-processed signature into segments; evaluating at least one observable on each said segment, thereby to obtain a respective feature value; and mapping each segment to a particular state of a hidden Markov model according to a rule, wherein: the rule tends to maximize the likelihood that the respective feature values were generated by a sequence of states defined by said mapping, and there are more segments than there are states, so that each signature will have more than unit duration in at least some states; the method further comprising: storing, as part of said model, a statistical distribution over the training signatures of the feature values corresponding to at least one said observable; CHARACTERIZED IN THAT the method further comprises: storing as part of said model, a statistical distribution over the training signatures of the duration in each of the states.