In　the　service　robot　application　scenario,　the　stable　grasp　requires　careful　balancing　the　contact　forces　and　the　property　of　the　manipulation　objects,　such　as　shape,　weight.　Deducing　whether　a　particular　grasp　would　be　stable　from　indirect　measurements,　such　as　vision,　is　therefore　quite　challenging,　and　direct　sensing　of　contacts　through　tactile　sensor　provides　an　appealing　avenue　toward　more　successful　and　consistent　robotic　grasping.　Other　than　this,　an　object＇s　shape　and　weight　would　also　decide　whether　to　grasping　stabilize　or　not.　In　this　work,　we　investigate　the　question　of　whether　tactile　information　and　object　intrinsic　property　aid　in　predicting　grasp　outcomes　within　a　multi-modal　sensing　framework　that　combines　vision,　tactile　and　object　intrinsic　property.　To　that　end,　we　collected　more　than　2550　grasping　trials　using　a　3-finger　robot　hand　which　mounted　with　multiple　tactile　sensors.　We　evaluated　our　multi-modal　deep　neural　network　models　to　directly　predict　grasp　stability　from　either　modality　individually　or　multi-modal　modalities.　Our　experimental　results　indicate　the　visual　combination　of　tactile　readings　and　intrinsic　properties　of　the　object　significantly　improve　grasping　prediction　performance.