Abstract: Near infrared (NIR) spectroscopy combined with successive projections algorithm (SPA) was investigated for determination of water content in oil. A total of 57 oil samples were scanned, the correlation coefficient (R) and root mean square error (RMSE) were used as the model evaluation indices, the full-spectrum partial least squares (PLS) model was developed for the prediction of water content in oil after the performance comparison of different pretreatments. Simultaneously, successive projections algorithm was applied for the extraction of effective wavelengths, the selected effective wavelengths were used as the inputs of partial least squares (PLS). The results indicated that a total of 24 variables, only 4.68 percent in the full spectrum, selected by SPA are employed to construct the model with 0.994 4 as the correlation coefficient and 5.455 110-5 as the root of mean square error of validation set, PA-PLS model is better than full-spectrum PLS model. An excellent prediction precision was achieved. In conclusion, successive projections algorithm is a powerful way for effective wavelength selection, and it is feasible to determine the water content in oil using near infrared spectroscopy and SPA-PLS, and an excellent prediction precision was obtained. This study supplied a new and alternative approach for further application of near infrared spectroscopy in on-line monitoring of contamination such as water content in oil.