Catalyst design and evaluation is a multifactorial multiobjective optimization problem and the absence of well-defined mechanistic relationships between wide ranging input-output variables has stimulated interest in the application of artificial neural network for the analysis of the large body of empirical data available. However, single ANN models generally have limited predictive capability and insufficient to capture the broad range of features inherent in the voluminous but dispersed data sources. In this study, we have employed a Fibonacci approach to select optimal number of neurons for the ANN architecture followed by a new weighted optimal combination of statistically-derived candidate ANN models in a multierror sense. Data from 200 cases for catalytic methane steam reforming have been used to demonstrate the veracity and robustness of the integrated ANN modeling technique. (C) 2011 American Institute of Chemical Engineers AIChE J, 58: 24122427, 2012