Catalytic plate microchannel reactors (CPRs) are a promising means for modular hydrogen/fuels production from distributed natural gas resources. However, the equipment miniaturization presents challenges for process control, including spatially-distributed models, limited availability of measurements, and fast process time constants. In the present paper, we investigate the use of data-driven models—specifically, artificial neural networks (ANNs)—to estimate temperature “hotspots” within CPRs. We prescribe nonlinear transformations of the model inputs in the form of well-known dimensionless quantities (e.g., Reynolds number), and we show that these engineered features can improve the prediction capability of computationally parsimonious ANNs using a first-principles reactor model. Finally, we present a simulation case study that demonstrates the use of a trained ANN for inferential model predictive control.