Native and non-native use of language differs, depending on the proficiency of the speaker, in clear and quantifiable ways. It has been shown that customizing the acoustic and language models of a natural language understanding system can significantly improve handling of non-native input; in order to make such a switch, however, the nativeness status of the user must be known. In this paper, we show how the recognition hypothesis can be used to predict with very high accuracy whether the speaker is native. Effectiveness of both word-based and phone-based classification are evaluated, and a discussion of the primary discriminative features is presented. In an LVCSR system in which users are both native and non-native, we have achieved a 15.6% relative decrease in word error rate by integrating this classification method with speech recognition.