Two methods are proposed by employing deep neural network for Uyghur large vocabulary continuous speech recognition : Hybrid architecture models are established with deep neural network (DNN) and hidden Markov model (HMM) for replacing Gaussian mixture model (GMM) in GMM-HMM to compute the state emission probabilities; DNN is facilitated as a front-end acoustic feature extractor to extract bottleneck feature(BN) to provide more effective acoustic features for the traditional GMM-HMM modeling framework(BN-GMM-HMM). The experimental results show that DNN-HMM and BN-GMM-HMM reduce word error rate(WER) by 8.84% and 5.86% compared with the GMM-HMM baseline system, which demonstrates that the two methods accomplish significant performance improvements.