Object picking with two-fingered grippers is widely used in practice. However, the deformability and slipperiness of the target object still remain a challenge, and not resolving them might lead to breaking or dropping of the grasped objects. To prevent such instances, tactile sensing plays an important role because it can directly detect even the subtle changes that occur during grasping. Mechanoreceptors in the human skin detect such events by the change in the skin shape and/or vibration. Using a similar approach, a combined deformation and slip detection system using a distributed 3axis tactile information with various time-scales is proposed. Specifically, the tactile information includes the z-axis data, which denotes the deformation of the skin perpendicular to the finger's surface and the x-and y-axes, which measure deformations tangential to the surface. The perpendicular and tangential tactile information are used to determine the deformation and slip, respectively. The system is based on a multilayer perceptron (MLP) that outputs detection results from a 3-axis tactile information. Results showed that, the perpendicular and tangential tactile information with an appropriate timescale were effective for deformation and slip detection with over 89% and 95% recognition rates, respectively, measured for 40 different objects. Moreover, 195 out of 200 real-time untrained grasping states were successful detected. Finally, 10 untrained objects were successfully picked.