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. INTRODUCTION Artificial Neural Networks (ANN) has been established as a tool for effortless computation and its application in environmental engineering field is very promising and has gained extensive interest (Hamoda et al., 1999; Hack and Kohne, 1996; Gontarski et al., 2000; Bongards, 2001; Hamed et al., 2004; Rene and Saidutta, 2008). ANN have been successfully employed in solving problems in areas such as fault diagnosis, process identification, property estimation, data smoothing and error filtering, product design and development, optimization, dynamic modeling and control of chemical processes, for the prediction of vapor-liquid equilibrium (VLE) data and estimation of activity coefficients. The purpose of using artificial neural networks in wastewater treatment system is to reduce the number of experiments that are being carried out to characterize the system. ANN has remarkable ability to derive meaningful information from complicated or imprecise data. It can be used to extract patterns and detect trends, which are too complex to be noticed by other computational technique (Mehrotra et al., 1997). Neural networks, inspired by the information processing strategies of the human brain, are proving to be useful in a variety of engineering applications. ANN may be viewed as paralleled computing tools comprising of highly organized processing elements called neurons which control the entire processing system by developing association between objects in response to their environment. The researches have proposed many architectures of the network .Two widely used network for modeling the non-linear problems in engineering systems are the Backpropagation and Radial Basis Function (RBF) networks.