Bringing tactile sensation to robotic hands will allow for more effective\ngrasping, along with the wide range of benefits of human-like touch. Here we\npresent a 3D-printed, three-fingered tactile robot hand comprising an OpenHand\nModel O customized to house a TacTip soft biomimetic tactile sensor in the\ndistal phalanx of each finger. We expect that combining the grasping\ncapabilities of this underactuated hand with sophisticated tactile sensing will\nresult in an effective platform for robot hand research -- the Tactile Model O\n(T-MO). The design uses three JeVois machine vision systems, each comprising a\nminiature camera in the tactile fingertip with a processing module in the base\nof the hand. To evaluate the capabilities of the T-MO, we benchmark its\ngrasping performance using the Gripper Assessment Benchmark on the YCB object\nset. Tactile sensing capabilities are evaluated by performing tactile object\nclassification on 26 objects and predicting whether a grasp will successfully\nlift each object. Results are consistent with the state of the art, taking\nadvantage of advances in deep learning applied to tactile image outputs.\nOverall, this work demonstrates that the T-MO is an effective platform for\nrobot hand research and we expect it to open-up a range of applications in\nautonomous object handling. Supplemental video: https://youtu.be/RTcCpgffCrQ.\n