A new approach is presented for clustering the speakers from unlabeled and unsegmented conversation, when the number of speakers is unknown. In this approach, each speaker is modeled by a Self- Organizing-Map (SOM). For estimation of the number of clusters the Bayesian Information Criterion (BIC) is applied. This approach was tested on the NIST 1996 HUB-4 evaluation test in terms of speaker and cluster purities. Results indicate that the combined SOM-BIC approach can lead to better clustering results than the baseline system.