Ergodic, continuous-observation, hidden Markov models (HMMs) were used to perform automatic language classification and detection of speech messages. State observation probability densities were modeled as tied Gaussian mixtures. The algorithm was evaluated on four multilanguage speech databases: a three language subset of the Spoken Language Library, a three language subset of a five language Rome Laboratory database, the 20 language CCITT database, and the ten language OGI telephone speech database. Generally, performance of a single state HMM (i.e. a static Gaussian mixture classifier) was comparable to the multistate HMMs, indicating that the sequential modeling capabilities of HMMs were not exploited.