Abstract: In order to recognize cigarette brands rapidly, a pattern recognition model for 10 cigarette brands made by China Tobacco Guizhou Industrial Limited Corporation was established based on the NIR spectral data of cut filler and machine learning method. The parameters in the model were optimized iteratively corresponding to the best recognition accuracy, and the model was verified with the collected data of cigarette samples. The results showed that by applying CWT to spectra processing, PPCA to data dimension reduction, and the SVM of linear kernel function to recognition model establishment, the RA value might reach 97.20%, which indicated that cigarette brands could be recognized accurately via the spectral data of cut filler. This technology provides a support for cigarette blend maintenance.