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Off-line signature verification rests on the hypothesis that each writer has similarity among signature samples, with small distortion and scale variability. This kind of distortion represents intrapersonal variability. This paper reports interpersonal and intrapersonal variability influences in a software approach based on a hidden Markov model (HMM) classifier. The experiments have shown error rate variability considering different forgery types, random, simple and skilled. The mathematical approach and resulting software also report considerations in a real application problem.