Detection of combustible gases is very important to reduce the modality and disability of human in both of civil and military situation. In this paper, a method of detection combustible gases of acetone and ethanol was proposed by using back propagation neural network (BPNN) and principal component analysis (PCA). The gas data were collected using some metal oxide semiconductor (MOS) gas sensors exposed to the mixture combustible gases of different concentration. The features of low and high frequency domain were extracted to establish a feature vector of 432 dimensions. Then PCA was used to reduce the dimension of feature vector from 432 to 11 which retained 99% information. The results showed the binary classification accuracy of BPNN is up to 100% for train, validation and test when distinguishing the combustible gas from the air. The mean and variance of error (0.004±0.008) for concentration prediction were obtained based on BPNN and PCA. The results demonstrated that the proposed method is effective for classification and concentration prediction of combustible gas.