Abstract: Currently,the sorting of coal and gangue mainly relies on manual sorting and mechanical sorting. These two methods are labor intensive,consume a large amount of energy,and cause environmental pollution. Therefore,in recent years,the research on coal and gangue sorting robots has drawn much attention. One of the key functions of the sorting robot is identifying coal and gangue,however,which still remains a crucial and difficult problem to be solved. This pa- per propose an image processing based method for the problem and further develops an identification system. The hardware composition of the system in the coal and gangue sorting robot is introduced,especially,the selection and installa- tion methods of the components of image identification system under the real-world condition are studied. Firstly a coal and gangue image repository is constructed by building image collection system and collecting the images of coals and gangues from Hancheng mining area. Then,an experiment is conducted to compare three kinds of filters for noise re- duction of the images,which indicates that the nonlinear low pass filtering achieves the best performance. Considering that the surfaces of coal and gangue differentiate in grayscale and texture,they are compared in terms of four parame- ters of grayscale and five parameters of texture, it is found that the coal and gangue are more distinct in the two grayscale parameters including gray average and the grayscale value corresponding to the maximum frequency,and oth- er two texture parameters including contrast and entropy than other parameters. Furthermore,LS-SVM is chosen as the image classifier. With the training of the classifier by inputting the two grayscale features,the two texture features and the combined features of grayscale and texture respectively,it is found that the classifier using the combined features has the best performance. The programs have been developed for the image collection,image filtering,combined feature vector extraction,and sample classification using LABVIEW. The identification system is built on the sorting robot ex- perimental platform. To evaluate the performance of the system,the images of coals and gangues are chosen,which are randomly picked from production environment. Furthermore,the nonlinear low pass filter is used for noise reduction and the combined features are used to train the classifier. The results show that the model achieves an accuracy of 90． 3% in identifying coals and 83% in identifying gangues,the averaged identification time is 0. 153 s.