Abstract: OBJECTIVE To study discrimination of thin-layer chromatograms of Chinese propolis and poplar tree gum using machine learning and to investigate a feasible strategy. METHODS Samples were extracted with methanol and the solutions were applied on Silica 60 plate. A mixture of n-hexane-ethyl acetate-formic acid(10:4:0.5) was used as the mobile phase. The plate was treated with the ammonia and hydrogen peroxide vapours until the colour of the spots on the plate was clear. Then the image of the thin-layer chromatogram was acquired by office scanner. Images of chromatograms were discriminated using machine learning. Densitometric chromatogram data were acquired from images by image analysis and discriminated using principal component analysis, partial least squares discrimination, linear discriminant analysis, separately. And the lane images of samples were separately cropped according to their migration paths, features of lane images were extracted by seven image feature descriptors separately, and data were used for training and discrimination using support vector machine. RESULTS The results of principal component analysis and partial least squares discrimination showed that there was obvious classification boundary between chromatographic data of Chinese propolis samples and those of poplar tree gum samples. And the correct rate of machine learning using linear discriminant analysis was 100%. For the methods combining features extraction using image feature descriptors and support vector machine, discrimination according to ColorLayout and Tamura descriptor obtained better results, and their correction rates were both 100%. CONCLUSION It is feasible to use machine learning in discriminant analysis of thin-layer chromatographic identification of Chinese propolis, and it would be beneficial to the discriminant objectivity of thin-layer chromatographic identification. The method combining features extraction using ColorLayout, Tamura image descriptor and support vector machine were convenient to use and feasible to be used in analysis based on machine learning for a great number of thin-layer chromatograms.