The application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of organic matters in the reactor. The neural network has been trained with experimental data obtained from an inverse fluidized bed reactor treating the starch industry wastewater. Experiments were carried out at various initial substrate concentrations of 2250, 4475, 6730 and 8910 mg COD/L and at different hydraulic retention times (40, 32, 24, 26 and 8h). It is found that neural network based model has been useful in predicting the system parameters with desired accuracy.