In this paper we explore the use of language- independent acoustic models for language identification (LID). The phone sequence output by a single language-independent phone recognizer is rescored with language-dependent phonotactic models approximated by phone bigrams. The language-independent phoneme inventory was obtained by Agglomerative Hierarchical Clustering, using a measure of similarity between phones. This system is compared with a parallel language-dependent phone architecture, which uses optimally the acoustic log likelihood and the phonotactic score for language identiffication. Experiments were carried out on the 4-language telephone speech corpus IDEAL, containing calls in British English, Spanish, French and German. Results show that the language-independent approach performs as well as the language-dependent one: 9% versus 10% of error rate on 10 second chunks, for the 4-language task.