Catalyst deactivation is one of the major concerns in industrial catalytic reactors. The capability to perform detailed analysis of the catalytic process and its deactivation phenomenon is therefore vital in maintaining high productivity and product quality. Analysis and prediction of the catalyst deactivation mechanism and the rate of deactivation are among the most challenging endeavors in the area of the catalytic reactor modeling. Hence, the catalyst deactivation phenomenon requires detailed dynamic modeling to enable its performance to be closely scrutinized. In this paper, a hybrid model incorporating first principle model and artificial neural network (ANN) has been used to develop a generic framework to model the industrial fixed-bed catalytic reactors (FBCRs) experiencing catalyst deactivation. The model does not consider the complicated mechanism of catalyst deactivation, and its effect is incorporated employing ANN, which utilizes the catalytic process data. The generic modeling steps have been extensively described, and the developed model has been applied on two industrial case studies. The validation of each model was carried out signifying that the generalized model developed has acceptable accuracy. The model enabled the lifetime of the industrial Pd/C and CuO-ZnO-Al2O3 catalysts and the effects of the operating parameters on the hydropurification process and methanol production reactors to be predicted. The generic strategy presented can be utilized for the performance analysis of any FBCR disregarding the type of catalyst. © 2016 Institution of Chemical Engineers