Hierarchical image structures are abundant in computer vision, and have been used to encode part structure, scale spaces, and a variety of multiresolution features. In this paper, we describe a unified framework for both indexing and matching such structures. First, we describe an indexing mechanism that maps the topological structure of a directed acyclic graph (DAG) into a low-dimensional vector space. Based on a novel eigenvalue characterization of a DAG, this topological signature allows us to efficiently retrieve a small set of candidates from a database of models. To accommodate occlusion and local deformation, local evidence is accumulated in each of the DAG’s topological subspaces. Given a small set of candidate models, we will next describe a matching algorithm that exploits this same topological signature to compute, in the presence of noise and occlusion, the largest isomorphic subgraph between the image structure and the candidate model structure which, in turn, yields a measure of similarity which can be used to rank the candidates. We demonstrate the approach with a series of indexing and matching experiments in the domains of 2-D and (view-based) 3-D generic object recognition.