In order to make full use of the image color information and overcome the traditional single channel pulse coupled neural network information loss in the process of image segmentation. A multi-channel image segmentation method is proposed, input channel of each color component is established for RGB color space. So multi-channel pulse coupled neural networks are formed which contains three input channels. Internal activity is modified based on coupled averaging of each input channel, dynamic threshold changes with exponential ascent, each component of the three-dimensional euclidean inverse distance matrix is calculated as the connection weighting coefficient matrix for each channel, and maximum entropy is adopted as evaluation criteria. Experiments are carried out based on standard color images, optimal parameters of multi-channel pulse coupled neural networks are selected according to test results. Experimental results show that more particulars of color image are preserved by color image segmentation based on multi-channel pulse coupled neural networks. Average value of maximum entropy increases by 3% relatively，image segmentation effect is improved while cost time is reduced more than 80%.