This　paper　presents　a　new　pruning　extreme　learning　machine　(N-PELM)　algorithm　which　can　generate　a　compact　single-hidden-layer　neural　network　(SLNN)　by　automatically　pruning　the　number　of　hidden　nodes　while　keep　high　accuracy.　The　proposed　N-PELM　algorithm　initializes　a　SLNN　by　using　extreme　learning　machine　(ELM)　algorithm　given　superfluous　number　of　hidden　nodes.　The　following　part　consists　of　two　iterative　processes.　First,　the　significance　of　the　hidden　node　is　estimated　and　the　insignificant　node　is　removed.　Secondly,　once　the　node　be　removed,　the　existing　hidden　nodes　are　updated　using　ELM　algorithm.　These　two　processes　continue　until　all　of　each　hidden　nodes　is　estimated　or　the　number　of　the　hidden　nodes　is　small　enough.　Compared　against　other　neural　network　algorithms,　N-PELM　algorithm　has　mainly　three　improvements.　Firstly,　the　significance　of　hidden　nodes　is　estimated　in　output　layer　such　that　the　relevance　of　hidden　nodes　and　classes　can　be　estimated　more　precisely.　Secondly,　the　pruning　threshold　is　selected　automatically　from　a　base　set　of　potential　relevance　threshold　values　using　Akaike　information　criterion　(AIC)　such　that　the　threshold　can　accommodate　any　data　type.　Thirdly,　P-ELM　uses　Kullback-Leibler　(KL)　divergence　and　Jensen　Shannon　(JS)　divergence　to　measure　the　significance　of　hidden　nodes.　Experimental　results　have　shown　that　the　P-ELM　algorithm　can　automatically　achieve　a　reasonable　compact　network　structure　while　keep　comparable　or　much　higher　accuracy　in　classification　and　regression.