Tactile information has been largely exploited for object recognition with robotic hands but very few approaches have used propri-oception alone. In those that do, raw values of joint angles or torques are exploited to train learning algorithms. However, these approaches under-exploit the potential of proprioception, such as its usefulness to estimate the object pose and size. Furthermore, they focus on recognizing individual objects, which increases the amount of data needed to train the algorithms. In this paper, we present an approach based only on joint angles of a robotic hand to generate a shape proprioceptive signature that is invariant to the size and position of the object. Instead of recognizing a specific object from a list, object characteristics useful for its manipulation are extracted. This signature is exploited not only for shape recognition but also for pose estimation. To illustrate the scope of this method, tests are performed on primitive shapes. Results show that the signatures are invariant within large ranges of sizes and poses. Experiments on real hand were carried, and results depicted that the method works similarly in both simulated environment and real applications. A comparison between this two results is made and discussed.