The metal hydride is a capable candidate for mobile storage for hydrogen-powered vehicles. An artificial neural network (ANN) has proved useful for many applications, and capable of much more in discovery of new materials. Because of its ability to generalize from examples presented to it, an ANN is a powerful tool for discovering new metal hydride combinations. An ANN can deduce quantitative structure property relationships for metal hydrides. The ANN found correlations between fundamental electronic and energy values modeled ab initio and several experimental parameters. Some of the properties successfully predicted with good correlation are: entropy, enthalpy, temperature at 1 atm of pressure, pressure at 25 C, and the percent weight of hydrogen stored. The marriage of ANN to computational modeling produces good predictions for many important properties of metal hydrides. Copyright (C) 2013, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.