This paper describes the experimental setup and the results obtained using several state-of-the-art speaker recognition classifiers. The comparison of the different approaches aims at the development of real world applications, taking into account memory and computational constraints, and possible mismatches with respect to the training environment. The NIST SRE 2008 database has been considered our reference dataset, whereas nine commercially available databases of conversational speech in languages different form the ones used for developing the speaker recognition systems have been tested as representative of an application domain. Our results, evaluated on the two domains, show that the classifiers based on i-vectors obtain the best recognition and calibration accuracy. Gaussian PLDA and a recently introduced discriminative SVM together with an adaptive symmetric score normalization achieve the best performance using low memory and processing resources.