It　is　difficult　to　achieve　real-time　accurate　measurement　for　effluent　biochemical　oxygen　demand　(BOD).　To　solve　this　problem,　a　soft-measurement　method　based　on　mutual　information　and　a　self-organizing　RBF　neural　network　is　proposed　for　BOD　prediction　in　this　paper.　First,　a　method　based　on　mutual　information　is　employed　to　extract　feature　variables,　and　these　variables　are　used　as　inputs　to　the　soft-measurement　model.　Second,　a　self-organizing　radial　basis　function　(RBF)　neural　network　based　on　error-correction　method　and　sensitivity　analysis　is　designed,　and　the　improved　Levenberg-Marquardt　(LM)　algorithm　is　used　to　train　parameters　of　the　neural　network　to　shorten　its　training　time.　Finally,　the　soft-measurement　model　is　applied　to　UCI　public　datasets　and　the　real　wastewater　treatment　process.　The　results　show　that　the　soft-measurement　model　has　a　more　compact　structure　and　relatively　short　training　time,　and　improves　the　prediction　accuracy,　which　realizes　a　fast　and　accurate　prediction　for　BOD.　©　All　Right　Reserved.