In　this　paper,　a　fast　growing　cascade　neural　network　(FGCNN)　is　proposed,　as　a　software　sensor,　to　rapidly　estimate　the　biochemical　oxygen　demand　(BOD)　in　wastewater　treatment　plants　(WWTPs).　Firstly,　a　novel　method,　based　on　the　orthogonal　least　squares　(OLS),　is　put　forward　to　add　input　and　hidden　units　to　the　existing　network　one　by　one.　Every　unit　added　to　the　network　affords　the　maximal　reduction　of　the　sum　of　squared　errors　(SSE).　Then,　the　FGCNN　incrementally　updates　its　output　weights　by　iterations　without　gradients　and　generalized　inverses,　while　the　other　weights　remain　unchanged　during　the　growth　of　the　network.　The　simple　and　effective　training　method　make　the　FGCNN　learn　extremely　fast.　Finally,　the　proposed　FGCNN　is　applied　to　estimate　the　BOD　in　WWTPs　using　other　easy-to-measure　or　secondary　variables.　The　experiment　results　show　that　the　FGCNN　has　better　performance　on　real-time　estimation　of　BOD　than　other　similar　methods.