In the context of speech and speaker recognition systems, it is well known that the combination of different feature streams can improve significantly their performance. However, the application of multi-stream (MS) techniques to speaker diarization systems has not been extensively studied. In this paper, we address this issue: we formulate different MS techniques, such as feature combination, probability combination and selection, for their specific application to the segmentation and clustering modules of a speaker diarization system. We evaluate the different methods proposed for the meetings domain (RT04s database) and two different pairs of streams: first, MFCC and PLP and second, MFCC and prosodic features. For both types of multi-streams, results show that the MS probability combination approach applied to the segmentation stage clearly outperforms the single-stream, MS feature combination and MS selection systems.