This　paper　focuses　a　system　to　fulfill　writer　identification,　which　identifies　a　person　with　his/her　handwriting.　A　text　independent　method　is　proposed　in　this　paper,　which　involves　no　local　feature　analysis.　First,　a　preprocessing　method　is　employed　to　normalize　script　images.　Then,　2-D　Gabor　wavelet　technique　is　developed　to　extract　the　global　features.　Finally　K-nearest　neighbor　(K-NN)　classifier　is　designed　to　identify　the　person　with　the　identified　handwriting.　Illustrative　experiments　are　made　with　110　specimens　of　50　people,　and　a　result　of　97.6%　accuracy　is　achieved　with　the　writer　identification　approach　proposed　in　this　paper.