In this research, near infrared (NIR) spectroscopy was used to detect procymidone in edible vegetable oils qualitatively. Edible vegetable oil samples with different procymidone contents were classified to two groups according to boundary line of maximum residue limit of procymidone in national standard. QualitySpec spectrometer was used to acquire spectra of two group samples, then uninformative variable elimination (UVE) and subwindow permutation analysis (SPA) were used to select informative wavelength variables. At last, several methods such as linear discriminant analysis (LDA), partial least squares-linear discriminant analysis (PLS-LDA) and discriminant partial least squares (DPLS) were used to develop classification models. The results indicate that NIR spectroscopy is feasible to classify the two group samples. UVE method can select informative wavelength variables effectively, and improve the performance of classification model. The best model is developed by UVE-DPLS method, the classification correct rate, sensitivity and specificity of prediction samples in this model are 98.7%, 95.0% and 100.0%, respectively.