In this study back-propagation, feed-forward neural networks are applied to estimate mass-transfer parameters in fast fluidized beds of fine solids. These networks are trained to predict mass-transfer rates using measurements of the sublimation rate of coarse naphthalene balls in fast fluidized beds of fine glass beads at several solid-to-gas mass flow rates within the relevant superficial gas-velocity range. When rested to predict the effective diffusivities from a coarse particle to the bulk of the fast bed of fine solids, trained neural networks calculated the Sherwood number with high accuracy. It is demonstrated that back-propagation, feed-forward neural networks provide a more accurate correlation for the mass-transfer coefficient compared to those obtained by the currently used heuristic models.