This paper is the second of two that together present a novel approach to the problem of reconstructing vascular trees from a small number of projections. Previously, we described the reconstruction algorithm and how it effectively circumvents the matching or "correspondence problem" found in most photogrammetric or computer-vision-based approaches. The algorithm is fully automatic and assumes that the imaging geometry is known, the vascular tree is a connected structure, and that its center-lines have been identified in three or more images. It employs consistency and connectivity constraints and comprises three steps: The first generates a connected structure representing the multiplicity of solutions that are consistent with the first two views; the second assigns a measure of agreement to each branch in this structure based on one or more additional projections; and the third step employs this measure to distinguish between those branches comprising the vasculature and the accompanying artifacts. This paper addresses the issue of validation via simulations and experiments. In addition to a clinical case, we examine the performance of the algorithm when applied to simulated projections of two 3-D vascular models, both representative of the complexity faced in coronary and cerebral angiography. The results in each instance are impressive and demonstrate that adequate reconstructions may be obtained with as few as three distinct views.