In this paper, three speaker verification procedures are tested. All the procedures are based on Gaussian mixture models (GMM), however, they differ in the way, in which they use particular feature vectors of an utterance for speaker verification. A lot of experiments have been performed in a group of 329 speakers. The results showed that there is a procedure that enables to achieve better results than the commonly used procedure based on the log likelihood of the whole utterance -- the procedure based on the majority voting rule for single feature vectors.