Understanding natural language queries is fundamental to many practical NLP systems. Often, such systems comprise of a brittle processing pipeline, that is not robust to "word salad" text ubiquitously issued by users. However, if a query resembles a grammatical and well-formed question, such a pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-well-formed categories and report an accuracy of 70.7% on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence model for generating questions for reading comprehension.