Mg-based hydrogen storage alloys are a type of promising cathode material of Nickel-Metal Hydride (Ni-MH) batteries. But inferior cycle life is their major shortcoming. Many methods, such as element substitution, have been attempted to enhance its life. However, these methods usually require time-consuming charge-discharge cycle experiments to obtain a result. In this work, we suggested a cycle life prediction method of Mg-based hydrogen storage alloys based on artificial neural network, which can be used to predict its cycle life rapidly with high precision. As a result, the network can accurately estimate the normalized discharge capacities vs. cycles (after the fifth cycle) for Mg(0.8)Ti(0.1)M(0.1)Ni (M = Ti, Al, Cr, etc.) and Mg(0.9-x)Ti(0.1)Pd(x)Ni (x = 0.04-0.1) alloys in the training and test process, respectively. The applicability of the model was further validated by estimating the cycle life of Mg(0.9)Al(0.08)Ce(0.02)Ni alloys and Nd(5)Mg(41)-Ni composites. The predicted results agreed well with experimental values, which verified the applicability of the network model in the estimation of discharge cycle life of Mg-based hydrogen storage alloys. Crown Copyright (c) 2008 Published by Elsevier Ltd on behalf of international Association for Hydrogen Energy. All rights reserved.