The　precision　of　soft　measurement　for　biochemical　oxygen　demand　(BOD)　is　always　restricted　due　to　various　factors　in　the　wastewater　treatment　plant　(WWTP).　To　solve　this　problem,　a　new　soft　measurement　modeling　method　based　on　chaos　theory　is　proposed　and　is　applied　to　BOD　measurement　in　this　paper.　Phase　space　reconstruction　(PSR)　based　on　Takens　embedding　theorem　is　used　to　extract　more　information　from　the　limited　datasets　of　the　chaotic　system.　The　WWTP　is　first　testified　as　a　chaotic　system　by　the　correlation　dimension　(D),　the　largest　Lyapunov　exponents　((1)),　the　Kolmogorov　entropy　(K)　of　the　BOD　and　other　water　quality　parameters　time　series.　Multivariate　chaotic　time　series　modeling　method　with　principal　component　analysis　(PCA)　and　artificial　neural　network　(ANN)　is　then　adopted　to　estimate　the　value　of　the　effluent　BOD.　Simulation　results　show　that　the　proposed　approach　has　higher　accuracy　and　better　prediction　ability　than　the　corresponding　modeling　approaches　not　based　on　chaos　theory.