Hand　gesture　plays　an　important　role　in　nonverbal　communication　and　natural　human-computer　interaction.　However,　the　complex　hand　gesture　structure　and　various　environment　factors　lead　to　low　recognition　rate.　For　instance,　hand　gesture　depends　on　individuals,　and　different　individuals＇　hands　are　with　different　sizes　and　postures,　in　addition,　unconstrained　environmental　illumination　also　influences　hand　gesture　recognition　performance.　Therefore,　hand　gesture　recognition　is　still　a　challenging　issue.　This　paper　proposes　a　robust　method　for　hand　gesture　recognition　based　on　convolutional　neural　network,　which　is　utilized　to　automatically　extract　the　spatial　and　semantic　feature　of　hand　gesture.　Our　method　consists　of　a　modified　Convolutional　Neural　Network　structure　and　data　preprocessing,　which　corporately　increase　hand　gesture　recognition　performance.　The　experimental　results　on　both　Cambridge　Hand　Gesture　Dataset　and　self-constructed　dataset　show　that　the　proposed　method　is　effective　and　competitive.　©　2016　IEEE.