Mobile query classification faces the usual challenges of encountering short and noisy queries as in web search. However, the task of mobile query classification is made difficult by the presence of more inter-active and personalized queries like map, command and control, dialogue, joke etc. Voice queries are made more difficult than typed queries due to the errors introduced by the automatic speech recognizer. This is the first paper, to the best of our knowledge, to bring the complexities of voice search and intent classification together. In this paper, we propose some novel features for intent classification, like the url's of the search engine results for the given query. We also show the effectiveness of other features derived from the part-of-speech information of the query and search engine results, in proposing a multi-stage classifier for intent classification. We evaluate the classifier using tagged data, collected from a voice search android application, where we achieve an average of 22% f-score improvement per category, over the commonly used bag-of-words baseline.