Motivated　by　the　knowledge　of　biological　neural　system　being　an　asymmetry　three　dimensional　structure　and　the　effect　of　inhibition　within　cerebral　cortex,　we　propose　a　novel　topology　of　artificial　neural　network　called　spatial　artificial　neural　network　(SANN),　which　includes　two　types　of　processing　networks:　basic　network　and　spatial　connection　network.　The　basic　network　introduces　the　lateral　inhibition　mechanism　between　hidden　units　and　realises　the　competition　in　neurons.　The　spatial　connection　means　that　any　two　neurons　in　SANN　may　have　random　and　long-range　connectivity.　Supervised　learning　rules　for　synaptic　weights　update　are　derived　from　the　steepest　descent　gradient,　and　the　descent　gradient　with　momentum　(GDM)　is　used　for　network　learning.　From　the　experimental　analysis　of　benchmark　problems　such　as　pattern　recognition,　non-linear　function　approximation,　we　prove　the　powerful　representation　capability　and　generalisation　performance　of　SANN　network.　Copyright　©　2011　Inderscience　Enterprises　Ltd.