Despite extensive research that has been done on sludge bulking, it remains a widespread problem in the operation of activated sludge processes, which brings severe economic and environmental consequences. In this study, a self-organizing radial basis function (SORBF) neural network method is utilized to predict the evolution of the sludge volume index (SVI). The hidden nodes in the SORBF neural network can be grown or pruned based on the node activity (NA) and mutual information (MI) to achieve the appropriate network complexity and maintain overall computational efficiency. The growing and pruning criteria of the SORBF can vary its structure dynamically with the objective to enhance its performance. Moreover, the input-output selection to calculate the SVI values is also discussed. The variables with key relations to the sludge bulking are used as the inputs for the SVI. Finally, the SORBF neural network is applied to the activated sludge wastewater treatment processes (WWTPs) for predicting the SVI, and then for predicting the sludge bulking. Experimental results show the excellent performance of the SORBF method. The performance comparison demonstrates the effectiveness of the proposed SORBF. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.