Three different speaker verification methods are described. All of them are based on Hidden Markov Models (HMM); the first one is of type text independent the other two are of type text prompted. The text independent method makes use of a single state Continuous HMM, to represent each customer in the system, while the text prompted methods require to use speaker dependent phoneme models. To model phonemes both Continuous HMMs and Semi-Continuous HMMs are used. Two different normalization methods for the likelihood values provided by the various HMMs are considered: one is based on the posterior probability, the other is based on the application of a mapping function.