Solids holdup and solids circulation rate are the two important hydrodynamic variables affected by process conditions. These two variables have a significant influence on the performance of a liquid-solid circulating fluidized bed (LSCFB). An artificial neural network (ANN) methodology was developed and simulated to predict the performance of the LSCFB for the experimental dataset collected under various process conditions. Different statistical parameters were applied to evaluate the prominent and unique characteristic features of the ANN-predicted parameters. The ANN model successfully predicted the experimental observations and captured the actual nonlinear behavior noticed during the experiments. Model validation confirmed that this data-driven technique can be used to model such nonlinear systems.