Sludge　bulking　is　one　of　the　most　serious　problems　in　the　Wastewater　Treatment　Plants　(WWTPs)　and　brings　about　significant　economic　loss.　Sludge　Volume　Index　(SVI),　a　key　sludge　sedimentation　performance　evaluation　index,　is　difficult　to　be　obtained　accurately　online.　To　monitor　the　SVI　value,　a　new　soft　sensor　modeling　method　based　on　Principal　Component　Analysis　(PCA)　and　Elman　Neural　Network　(ElmanNN)　is　proposed　in　this　paper.　The　final　inputs　of　model　are　determined　by　PCA.　Then,　the　SVI　value　is　modeled　by　the　Elman　network　in　the　WWTPs.　Finally,　compared　with　other　neural　networks,　the　experimental　results　show　that　Elman　network　is　more　efficient　in　modeling　the　SVI.　The　scale　of　network　can　be　simplified　and　its　capability　of　dealing　with　dynamic　information　can　be　strengthened.