The bottleneck features extracted from deep neural network not only have long term contextdependence and compact representation of speech signal, but also can replace the traditional MFCC features for GMM-HMM acoustic modeling. The authors apply bottleneck features and their concatenated features with MFCC into Lhasa-Tibetan continuous speech recognition. The experiments in Lhasa-Tibetan continuous speech recognition show that the concatenated features of bottleneck features and MFCC achieve better performance than the posterior features of deep neural network and mono-bottleneck features.