Mineral processing is a crucial stage in the mining process, involving comminution and concentration stages. Comminution is modeled using various ore variables and operational parameters, representing a complex system. An alternative to simplifying the complexity of these stages is adopting machine learning (ML) techniques; however, ML often requires a substantial amount of data for effective training and validation. The conjoint analysis methodology was used to develop a procedure for discretizing input variables and reducing the data needed for training neural networks, requiring only 77 different scenarios. Using the results from a comminution plant simulator built in Matlab Simulink, neural networks were trained to predict the key output parameters, such as the water consumption, energy consumption, operational parameters, and particle size generated by the plant. The predictive capability of the neural networks was excellent, achieving R² > 0.99 in all cases. The networks were tested with a new set of scenarios to assess their response to values not categorized in the discretization process, achieving R² > 0.98. However, the prediction capability was lost for out-of-range input variables. This approach is attractive for developing easy-to-implement ML tools capable of representing complex systems without needing large amounts of input data, thereby simplifying the modeling process in mineral processing.