This　paper　presents　a　novel　neural　network　learning　algorithm,　the　tabu-based　neural　network　learning　algorithm　(TBBP).　In　our　work,　the　TBBP　mainly　use　the　tabu　search　(TS)　to　improve　the　nonlinear　function　approximating　ability　of　the　neural　network.　By　using　the　TS　in　the　global　search,　the　algorithm　can　escape　from　the　local　minima　and　obtain　some　superior　global　solutions,　the　weights　of　the　neural　network,　to　approximate　the　nonlinear　function.　Results　confirm　that　the　TBBP　can　greatly　improve　the　approximating　ability　of　the　neural　network　for　several　typical　nonlinear　functions.　(c)　2006　Published　by　Elsevier　B.V.