The model of near infrared spectroscopy of water content in oil is complex, nonlinear, and difficult to be specified with mathematical methods. Uninformative variables elimination was applied to the extraction of effective wavelengths, and fuzzy C-means clustering algorithm was applied to obtain the input space location and the clustering center. By identifying the consequent parameters with recursive least-squares method, Takagi-Sugeno, a fuzzy model of near infrared spectroscopy of water content in oil, was established. This identification algorithm was compared with Partial Least Squares model and tested by experimental data. The results indicate: the Takagi-Sugeno model, constructed by a total of 34 variables selected by uninformative variables elimination, can accurately reflect the relation between near infrared spectral data of oil and moisture content; the correlation coefficient the model predicted for the samples from validation set is 0.964 6 and the root of mean square error is 1.531 2×10－4, which are satisfactory. The experimental results verify that it is feasible to detect the water content in oil by means of near infrared spectroscopy, which also offers a new alternative approach for the on-line monitoring of other contamination content in oil.