A new technique for neural-network-based modeling of chemical processes is proposed. Stacked neural networks allow multiple neural networks to be selected and used to model a given process. The idea is that improved predictions can be obtained using multiple networks, instead of simply selecting a single, hopefully optimal network, as is usually done. A methodology for stacking neural networks for plant-process modeling has been developed. This method is inspired by the technique of stacked generalization proposed by Wolpert. The proposed method has been applied and evaluated for three example problems, including the dynamic modeling of a nonlinear chemical process. Results obtained demonstrate the promise of this approach for improved neural-network-based plant-process modeling.