Recent results in the area of language identification have shown a significant improvement over previous systems. In this paper, we evaluate the related problem of dialect identification using one of the techniques recently developed for language identification, the Gaussian mixture models with shifted-delta-cepstral features. The system shown is developed using the same methodology followed for the language identification case. Results show that the use of the GMM techniques yields an average of 30% equal error rate for the dialects in the Miami corpus and about 13% equal error rate for the dialects in the CallFriend corpus.