Measurements of geometrical information (e.g., contact position and normal as well as the curvature of an object) and physical information (e.g., contact force) at fingertips are essential for a multifingered robot hand to perform dexterous grasping and manipulation tasks. This paper presents a low-cost fingertip sensor that can simultaneously measure the contact position and normal, the 3-D contact force, and the local radius of curvature (ROC) of the object in contact with the fingertip. The fingertip is a hemisphere made of silicone, and there are marker points on its internal surface. A camera is installed beneath the fingertip to capture the markers’ displacements, from which the aforementioned geometric and physical quantities are learned using artificial neural networks. Experiments on a handmade prototype show that, with such a simple setting, errors in the measured contact position and contact force can be as low as 1 mm and 0.3 N, respectively, and the ROC measurement can be very accurate.