This paper presents a neural network model of stereoscopic vision, in which a process of fusion seeks the correspondence between points of stereo inputs. Stereo fusion is obtained after a self-supervised learning phase, so called because the learning rule is a supervised-learning rule in which the supervisory information is autonomously extracted from the visual inputs by the model. This supervisory information arises from a global property of the potential matches between the points. The proposed neural network, which is of the cooperative type, and the learning procedure, are tested with random-dot stereograms (RDS) and feature points extracted from real-world images. Those feature points are extracted by a technique based on the use of sigma-pi units. The matching performance and the generalization ability of the model are quantified. The relationship between what have been learned by the network and the constraints used in previous cooperative models of stereo vision, is discussed.