In order to improve the accuracy of image classification and the conve rgence speed of multi-layer perceptron (MLP),this paper presents a novel image classification algorithm based on biogeography-based opt imization (BBO) MLP and texture fe atures.The proposed algorithm steps are as follows.Firstly,three kinds of different images from the image da tabase are selected,and the operating environment of the image classification algorithm is modeled.Secondly,4texture parameters,including UNI,ENT, CON and COR,are selected to obtain 4-dimens ion feature moments,and the training sample files are generated according to the category number provided by the customer and image texture feature vector.Thirdly,defining the evaluation of the habitat error as the fitne ss function,the data are used to train MLPs using BBO algorithm,and the classification model is obtained. Finally,the trained MLPs are used to image classification.Further,feedback mechanism is introduced to improve the performance.The experimental results show that,comparing with the current heuristic learning alg orithms of PSO,GA,ACO, ES and PBIL algorithms,the proposed BBO-MLP method is more effective and feasib le,which has higher classification precision compared with other approaches.