A rapidly and pollution-free method was developed to identify the types of waste water by visible/near-infrared spectroscopy and back-propagation artificial neural network (BP-ANN) algorithm.The spectra data of the total 168 samples were obtained by a FieldSpec3 spectrometer.All the samples were divided randomly into two groups,one with the 132 samples used as the calibrated set,and the other with the 36 samples as the validated set,and subsequently were analyzed with the whole wave band(400~2450 nm) and the selection wave band(400~1800 nm) models,respectively.The spectra data were pretreated by the methods of S.Golay Smoothing and Standard Normal Variable (SNV),and the pretreated spectra data were analyzed with Principal Component Analysis (PCA).The anterior 9 principal components computed by PCA were used as the input variables of BP-ANN model which included one hidden layer,while the values of the types of waste water used as the output variables,and consequently the three layers BP-ANN identification model was built.The 36 unknown samples in the validated set were predicted by the ANN-BP model.The results showed that the recognition rate was 100% in such both models,and the accuracy of selection wave band model was higher than that of the whole wave band model.We suggested that it was feasible to discriminate the types of waste water used by visible / near-infrared spectroscopy and BP-ANN algorithm as a rapid and pollution-free way,and the wave band selection was a validated way to improve the precision of the identification model.