The denitrifying sulfide removal (DSR) process has complex interactions between autotrophic and heterotrophic denitrifers: thus. constructing a detailed mechanistic model and proper control architecture is difficult. Artificial neural networks (ANNs) are capable of inferring the complex relationships between input and output process variables without a detailed characterization of the mechanisms governing the process. This work presents a novel ANN that accurately predicts the steady-state performance of an expended granular sludge bed (EGSB)-DSR bioreactor for nitrite denitrification and the complete DSR process. The proposed ANN shows that at a threshold hydraulic retention time (HRT) < 7 h, influent sulfide concentration markedly affects reactor performance. (C) 2009 Elsevier B.V. All rights reserved.