In this paper, we present the Normalized Cross Likelihood Ratio (NCLR) and the advantages of using it in a speaker diarization system. First, the NCLR is used as a dissimilarity measure between two Gaussian speaker models in the speaker change detection step and its contribution to the performance of speaker change detection is compared with those of BIC and Hostelling's T2-Statistic measures. Then, the NCLR measure is modified to deal with multi-gaussian adapted models in the cluster recombination step. This step ends the step-by-step speaker diarization process after the BIC-based hierarchical clustering and the Viterbi re-segmentation steps. By comparing the NCLR measure with the CLR (Cross Likelihood Ratio) one, more than 30% of relative diarization error is reduced in ESTER evaluation data.