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.