Prediction　of　bulking　sludge　is　a　matter　of　growing　importance　around　the　world.　In　this　study,　to　detect　bulking　sludge　of　wastewater　treatment　process　(WWTP),　an　intelligent　detection　method,　using　a　self-organizing　recurrent　radial　basis　function　neural　network　(SORRBFNN)　and　a　cause　variables　identification　(CVI)　algorithm,　was　developed　to　detect　the　fault　points　and　the　fault　variables　of　bulking　sludge.　For　this　intelligent　detection　method,　first,　the　structure　and　parameters　of　SORRBFNN　were　updated　by　an　information-oriented　algorithm　(IOA)　and　an　improvedLevenberg-Marquardt　(LM)　algorithm　to　improve　the　prediction　accuracy　of　the　sludge　volume　index　(SVI)　from　the　water　qualities.　Second,　the　CVI　algorithm　was　designed　to　allow　a　quick　revealing　of　the　cause　variables　of　bulking　sludge　with　high　accuracy.　And　the　intelligent　detection　method　was　tested　on　the　measured　data　from　a　real　WWTP.　Experimental　results　confirmed　the　attractiveness　and　effectiveness　of　the　proposed　intelligent　detection　method.　(C)　2018　Elsevier　Ltd.　All　rights　reserved.