This paper discusses a network for computer vision which comprises multiple nodes, each node indicating one object point and its corresponding multiple imaging rays. The motivation behind this multi-image network approach are threefold: 1) the use of redundancy to afford less reliance on scene intelligence; 2) the pursuit of quality through the quantity of information available in overdetermined systems; and 3) the treatment of networks of images rather than independent image pairs. The primary characteristics of a network vision scheme for 3D object reconstruction include application of epipolar curve constraints, use of multi-ray triangulation residuals in object space, adoption of least-squares network optimisation and application of global quality control measures. The matching speed for object point determination in the reported network vision implementation, comprising four images, reaches 120 points per second using a Pentium-200 processor. A 3D triangulation accuracy of close to 0.1 pixels is achieved.