In order to properly design and scale up spouted beds, one needs to predict the minimum spouting velocity of specific systems having different bed dimensions, and properties of particle and spouting gas. Because of inherent complexity of predicting minimum spouting velocity, the prevailing approach has been to use empirical correlations, a number of which are available in the literature. Central jet distributors are commonly used in the experimental studies reported in the literature. Circular slit distributor is a new concept in which air is supplied to the bed of particles through a circular slit. This paper presents results of an experimental study on the hydrodynamics of central jet and circular slit distributors. In this paper a fully connected feed-forward neural network model was used to predict the minimum spouting velocity of central jet and circular slit spouted beds. A neural network model was also developed to predict minimum fluidization velocity. The actual experimental data obtained from published literature and from the experiments carried out in this study were used for training and validating the models. The minimum spouting and fluidization velocities predicted using the neural network models developed in this study show a better approximation to the actual experimental values than those obtained from correlations available in the open literature. The position of flow regime of circular slit spouted bed was also established relative to the flow regimes of central jet spouted bed and fluidized bed. (c) 2005 Elsevier Ltd. All rights reserved.