This paper presents several methods for topic detection on newspaper articles, using either a general vocabulary or topic-specific vocabularies. Specific vocabularies are determined manually or statistically. In both cases, we aim at finding the most representative words of a topic. Several methods have been experimented, the first one is based on perplexity, this method achieves a 100% topic identification rate, on large test corpora, when the two first propositions are taken into account. Other methods are based on statistical counts and achieve 94% of identification on smaller test corpora. The most challenge of this work is to identify topics with only few words in order to be able, during speech recognition, to determine the best adequate language model.