Brain　tumor　segmentation　is　one　of　the　main　challenging　problems　in　computer　vision　and　its　early　diagnosis　is　critical　to　clinics.　Segmentation　needs　to　be　accurate,　efficient　and　robust　to　avoid　influences　caused　by　various　large　and　complex　biases　added　to　images.　This　paper　proposes　a　multiple　convolutional　neural　network　(CNNs)　framework　with　discrimination　mechanism　which　is　effective　to　achieve　these　goals.　First　of　all,　this　paper　proposes　to　construct　different　triplanar　2D　CNNs　architecture　for　3D　voxel　classification,　greatly　reducing　segmentation　time.　Experiment　is　conducted　on　images　provided　by　Multimodal　Brain　Tumor　Image　Segmentation　Benchmark　(BRATS)　organized　by　MICCAI　2013　for　both　training　and　testing.　As　T1,　T1-enhanced,　T2　and　FLAIR　MRI　images　are　utilized,　multimodal　features　are　combined.　As　a　result,　accuracy,　sensitivity　and　specificity　are　comparable　in　comparison　with　manual　gold　standard　images　and　better　than　state-of-the-art　segmentation　methods.