Convolutional　neural　networks　are　global　trainable　multi-stage　architectures　that　automatically　learn　translation　invariant　features　from　raw　input　images.　However,　in　tradition　they　only　allow　adjacent　layers　connected,　limiting　integration　of　multi-scale　information.　To　further　improve　their　performance　in　classification,　we　present　a　new　architecture　called　shortcut　convolutional　neural　networks.　This　architecture　can　concatenate　multi-scale　feature　maps　by　shortcut　connections　to　form　the　fully-connected　layer　that　is　directly　fed　to　the　output　layer.　We　give　an　investigation　of　the　proposed　shortcut　convolutional　neural　networks　on　gender　classification　and　texture　classification.　Experimental　results　show　that　shortcut　convolutional　neural　networks　have　better　performances　than　those　without　shortcut　connections,　and　it　is　more　robust　to　different　settings　of　pooling　schemes,　activation　functions,　initializations,　and　optimizations.