This paper deals with an approach to Automatic Language Identification based on rhythmic and fundamental frequency modeling. Experiments are performed on read speech for 5 European languages. They show that rhythm can be automatically extracted and is relevant in language identification: using cross-validation, 79% of correct identification is reached with 21 s. utterances. The fundamental frequency modeling, tested in the same conditions (cross-validation), produces 50% of correct identification for the 21 s. utterances. The Vowel System Modeling gives an identification rate of 70% for the 21 s. utterances. Last, merging the three models slightly improves the identification rate.