In this paper, we report on the performance of two variants of wellknown statistical-based clustering techniques and present an evaluation on the TIMIT and TI-Digit databases. A clustering approach which 1) is based on a divergence criterion, 2) separates "good" and "bad" models using a class-dependent adjustable threshold on the number of examples per model, and 3) guides the clustering by limiting the number of models per class between two constants Nmin and Nmax, gave the best results. On the TI-Digit database, the combination of triphone modeling and divergence-based clustering yielded greater accuracy than that obtained with word models for a similar system complexity.