In　this　paper,　an　efficient　algorithm　based　on　the　pruning　method　and　the　Levenberg　Marquardt　(LM)　is　presented　to　design　the　single　hidden　layer　feedforward　neural　network　(FNN).　This　new　approach　can　prune　the　redundant　hidden　nodes　by　calculating　the　Hessian　and　removing　the　lines　in　the　matrix　for　reconstructing　the　FNN.　The　proposed　pruning　hidden　nodes　(PHN)　algorithm　can　adjust　the　parameters　of　the　neural　networks　as　well.　The　proposed　PHN　is　simple　and　effective　and　generates　a　FNN　model　with　a　high　accuracy　and　compact　structure.　In　addition,　the　convergence　of　both　the　structures　dynamic　process　and　after　the　modifying　is　discussed.　The　PHN　is　then　tested　on　the　non-linear　functions　approximation　to　illustrate　the　effectiveness　of　our　proposed　reconstructing　scheme.　Finally,　the　PHN　is　employed　to　model　chemical　oxygen　demand　(COD)　concentration　in　the　wastewater　treatment　process.　Experimental　results　show　that　the　proposed　method　is　efficient　for　network　structure　pruning　and　it　achieves　better　performance　than　some　of　the　existing　algorithms.　©　2011　by　IJAI.