This study investigates the feasibility of using artificial neural networks (ANNs) to predict catalytic oxidation in diesel after-treatment systems and compares their performance to that of physics-based models. Existing physics models are revisited to generate baseline data for binary reactions of major species (CO, C3H6, and NO) measured in a lab-scale microreactor comprising a metallic foam catalytic substrate. The physics model performs well to predict the measured light-off curves, which are the species conversions with ramping temperature, and the R2 value is above 0.84 across a wide range of operating conditions. However, the model cannot perfectly capture the retarding trends observed in the CO and C3H6 conversion curves after light-off. In contrast, the ANN model is capable of accurately predicting the light-off curves for operating conditions seen during the training process. This might be practically useful but is inherently limited by the availability of experimental data for training. To compensate for the drawbacks of both approaches, this study suggests a hybrid model in which a pretrained ANN is used to calculate reaction rates in the physics models. Despite the more complex data generation process for training ANNs, the hybrid model captures the light-off curves including the retarding trend and is less sensitive to the range of test conditions without renormalization as compared to the pure ANN model. This study investigates the feasibility of ANNs by comparing the pros and cons among the physics models, pure ANN, and hybrid models and suggests a step toward the most appropriate uses of ANNs in modeling exhaust after-treatment in practical applications.