In this paper, we study the use of morphological and syntactic context features to improve speech recognition of a morphologically rich language like Arabic. We examine a variety of syntactic features, including part-of-speech tags, shallow parse tags, and exposed head words and their non-terminal labels both before and after the word to be predicted. Neural network LMs are used to model these features since they generalize better to unseen events by modeling words and other context features in continuous space. Using morphological and syntactic features, we can improve the word error rate (WER) significantly on various test sets, including EVAL'08U, the unsequestered portion of the DARPA GALE Phase 3 evaluation test set. ©2010 IEEE.