In　this　paper,　a　soft　computing　method,　based　on　a　recurrent　self-organizing　neural　network　(RSONN)　is　proposed　for　predicting　the　sludge　volume　index　(SVI)　in　the　wastewater　treatment　process　(WWTP).　For　this　soft　computing　method,　a　growing　and　pruning　method　is　developed　to　tune　the　structure　of　RSONN　by　the　sensitivity　analysis　(SA)　of　hidden　nodes.　The　redundant　hidden　nodes　will　be　removed　and　the　new　hidden　nodes　will　be　inserted　when　the　SA　values　of　hidden　nodes　meet　the　criteria.　Then,　the　structure　of　RSONN　is　able　to　be　self-organized　to　maintain　the　prediction　accuracy.　Moreover,　the　convergence　of　RSONN　is　discussed　in　both　the　self-organizing　phase　and　the　phase　following　the　modification　of　the　structure　for　the　soft　computing　method.　Finally,　the　proposed　soft　computing　method　has　been　tested　and　compared　to　other　algorithms　by　applying　it　to　the　problem　of　predicting　SVI　in　WWTP.　Experimental　results　demonstrate　its　effectiveness　of　achieving　considerably　better　predicting　performance　for　SVI　values.　(C)　2015　Elsevier　B.V.　All　rights　reserved.