It　is　difficult　to　measure　the　online　values　of　biochemical　oxygen　demand　(BOD)　due　to　the　characteristics　of　nonlinear　dynamics,　large　lag　and　uncertainty　in　wastewater　treatment　process.　In　this　paper,　based　on　the　knowledge　representation　ability　and　learning　capability,　an　improved　T-S　fuzzy　neural　network　(TSFNN)　is　introduced　to　predict　BOD　values　by　the　soft　computing　method.　In　this　improved　TSFNN,　a　K-means　clustering　is　used　to　initialize　the　structure　of　TSFNN,　including　the　number　of　fuzzy　rules　and　parameters　of　membership　function.　For　training　TSFNN,　a　gradient　descent　method　with　the　momentum　item　is　used　to　adjust　antecedent　parameters　and　consequent　parameters.　This　improved　TSFNN　is　applied　to　predict　the　BOD　values　in　effluent　of　the　wastewater　treatment　process.　The　simulation　results　show　that　the　TSFNN　with　K-means　clustering　algorithm　can　measure　the　BOD　values　accurately.　The　algorithm　presents　better　approximation　performance　than　some　other　methods.　(C)　2014　The　Chemical　Industry　and　Engineering　Society　of　China,　and　Chemical　Industry　Press.　All　rights　reserved.