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Abstract Hydrogen (H 2) is considered a clean valuable energy source and its worldwide demand has increased in recent years. The Water-Gas Shift (WGS) Reaction is one of the major routes for hydrogen production and uses different catalysts depending on the operating process conditions. A catalyst is usually composed of an active phase and a support for its dispersion. There are currently an increasing number of researches on catalytic field focusing on transition metals nanoparticles supported on different compounds. In order to predict optimal catalyst compositions for the WGS reaction, Artificial Neural Networks (ANNs) were used to build a model from the literature catalytic data. A three-layer feedforward neural network was employed with active phase composition and support type as some of the input variables, and Carbon Monoxide (CO) conversion as output variable. The insertion of properties such as surface area, calcination temperature and time allowed predicting the reaction performance based on intrinsic catalyst variables not commonly used in phenomenological kinetic models. Also, unlike previous studies, a detailed sensitivity analysis was carried out to observe useful trends. An important outcome of this work is the proposition of ceria-supported catalysts for the WGS reaction that present larger surface areas, with Ru, Ni or Cu as active phases conducted at moderate temperatures (≈300 °C) and with reasonable space velocities (2000–6000 h−1). In addition, it was possible to predict the most relevant variables for the process: the temperature and the surface area. Thus, the results show the power of ANNs for predicting better catalysts and conditions for this important process in the environmental field. Graphical abstract Image 1 Highlights • An ANN model for catalyst selection for the Water-Gas Shift reaction was developed. • Changes in CO conversion were investigated by performing a sensitivity analysis. • The model can predict better catalysts and operating conditions for the process. • The temperature and the surface area are the most significant variables. [ABSTRACT FROM AUTHOR]