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Aiming at the problem that the traditional intelligent fault diagnosis method has low diagnostic accuracy in the fault diagnosis of rolling bearings, a classification algorithm for rolling bearing fault diagnosis based on bat algorithm (BA) optimized Extreme Learning Machine (ELM) is proposed. Firstly, five time-domain dimensionless parameters of rolling bearing vibration signal are selected as inputs of the model, and then the input weight and hidden layer bias of ELM was optimized by BA with powerful capability in global-optimization, thus the most superior diagnosis model of ELM is obtained to be validated by the actual Rolling experimental data from Case Western Reserve University. The experimental results show that compared with the BP,SVM and ELM, the proposed method can improve the accuracy of fault diagnosis, and the accuracy of fault diagnosis reaches up to 99.17%.