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Objective To study the application of hidden Markov model(HMM) and autoregressive(AR) model in detection of epileptic electroencephalogram( EEG). The automated epileptic EEG recognition method can reduce the workload of doctors and reduce the influence of subjective factors of artificial identification. Methods This paper used the optimal order AR model which is based on combined information criterion (CIC) criterion to do the EEG feature extraction,and used CD-HMM as the classification tool. The clinical EEG data of Nanjing General Hospital of Nanjing Military Command (8 groups of 16 leads normal and epileptic EEG signals with a sampling frequency of 512Hz) were analyzed and identified. In the experiment,the six lead (T3,T4,FP1,FP2,C3,C4) data of each sample were selected. HMM modeling was performed using 15 samples of training concentration,and the remaining 35 sections were used for testing. Results The recognition rate of epilepsy EEG reached 92.8%. Conclusions The recognition rate is higher under the optimal order AR model and CD-HMM,which have certain application prospect in the development of brain-machine interface equipment.