Microarray　technology　has　become　a　great　source　of　information　for　biologists　to　understand　the　workings　of　DNA　which　is　one　of　the　most　complex　codes　in　nature.　Microarray　images　typically　contain　several　thousands　of　small　spots,　each　of　which　represents　a　different　gene　in　the　experiment.　One　of　the　key　steps　in　extracting　information　from　a　microarray　image　is　the　segmentation　whose　aim　is　to　identify　which　pixels　within　an　image　represent　which　gene.　This　task　is　greatly　complicated　by　noise　within　the　image　and　a　wide　degree　of　variation　in　the　values　of　the　pixels　belonging　to　a　typical　spot.　In　the　past　there　have　been　many　methods　proposed　for　the　segmentation　of　microarray　image.　In　this　paper,　a　new　method　utilizing　a　series　of　artificial　neural　networks,　which　are　based　on　multi-layer　perceptron　(MLP)　and　Kohonen　networks,　is　proposed.　The　proposed　method　is　applied　to　a　set　of　real-world　cDNA　images.　Quantitative　comparisons　between　the　proposed　method　and　commercial　software　GenePix®　are　carried　out　in　terms　of　the　peak　signal-to-noise　ratio　(PSNR).　This　method　is　shown　to　not　only　deliver　results　comparable　and　even　superior　to　existing　techniques　but　also　have　a　faster　run　time.　©　2013　Elsevier　Ireland　Ltd.