Mineral pigments are widely used in the ancient Chinese painting. Classification and identification of mineral pigments are important for cultural heritages conservation. As a non-destructive method, hyperspectral classification is based on the knowledge that different mineral pigments have distinct reflection spectra. This study acquired the hyperspectral images of 38 mineral pigments and established a reflection spectral library. Then Spectral Angle Mapper (SAM) was used to classify the test data and 0.20 was selected as the optimal threshold. For pigments having similar color and spectra, SAM was unable to classify them correctly. Therefore, decision tree, a machine learning method, was applied to the classification of the pigments misclassified by SAM. For each pigment, 7500 samples were randomly selected as training data and 2500 samples were selected as test data. Though the ID3 algorithm, a decision tree for pigment classification was learned. Then test data was classified by the decision tree. Compared with SAM, the accuracy of classification observed from decision tree was obviously improved. For most pigments, the accuracy of decision tree reached 94%. The results revealed that the SAM combined with decision tree could effectively achieve a discrimination of all the 38 mineral pigments in the experiment, thus providing a new approach for mineral pigments classification.