This paper is concerned with the problem of Robust Speaker Recognition. An acoustical mismatch between training and testing conditions of hidden Markov model (HMM)-based speaker recognition systems often causes a severe degradation in the recognition performance. In telephone speaker recognition, for example, undesirable signal components due to ambient noise and channel distortion, as well as due to different variations of telephone handsets render the recognizer unusable for real-world applications. The purpose of this paper is to present several compensation techniques to decrease or to remove the mismatch between training and testing environment conditions. Some of the techniques described here have already been successfully applied in Robust Speech Recognition, and our preliminary results show that they are also very encouraging for Speaker Recognition.