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Automatic language identification has become an important issue in recent years in speech recognition systems. In this paper, we present the work done in language identification for an air traffic control speech recognizer for continuous speech. The system is able to distinguish between Spanish and English. We present several language identification techniques based on full recognition that improve the baseline results obtained using the most commonly known "PPRLM" technique. We have in our database some task specific critical problems for language identification like non native speakers, extremely spontaneous speech or Spanish-English mix in the same sentence. We confirm that PPRLM is quite sensible to those problems and that a technique based on a Bayesian classifier is the one with the best performance in spite of its higher computational cost.