Conflation and co-registration are critical applications for NASA, especially for co-registration of datasets differing in scale, resolution, sensor spectrum range (e.g. optical with IR or thermal imagery). Traditional georegistration solutions in the geospatial, photogrammetric, and computer vision communities select a set of control features in an existing database and identify the same features in an incoming dataset. However, these registration algorithms are not robust due to variations in scale, resolution, and sensor characteristics, where the identification of control features becomes a complex and challenging process. Recent NASA sponsored innovations include Feature Analyst (FA) automation with a registration scheme that is along the lines of traditional tools (e.g. use few control features to co-register two datasets). Here, we propose a new approach based object configuration similarity. Our solution - a departure from traditional approaches ? uses abstract spatial relations (e.g. three square buildings forming an orthogonal triangle with a river running between them) as matching features, and transforms the registration problem into a spatial similarity assessment problem. Our approach results in unparalleled pull-in range as coarse location data are adequate to support the recovery of registration information for a configuration.