The application of the neural network to remote sensing infrared chemical detection is introduced. The possibility that the back-propagation artificial neural network (BP-ANN) classifier is implemented to discriminate infrared spectra is approached. A data set of DMMP (dimethyl methylphosphonate) infrared spectra is used to perform the actual training and discrimination performence prediction of this classifier. The training results show that 95% samples in the data set can be correctly classified under certain conditions. The prediction results show that the neural network classifier which has been properly trained has possessed relatively high discrimination ability, and the discrimination rates more than 70% can be obtained from such classifiers.