As semiconductor lithography geometries scale deeper into submicron regimes, the importance of abundant and accurate metrology becomes more apparent. For example, processes such as OPC generation, RET selection and structure validation require detailed image information such as CD, area, slope and line edge roughness (LER) in order to be considered robust. Large numbers of SEM images of different structure types are often required in order to gain physical insight into pattern transfer behavior (proximity, resolution limits, etc.). In this paper, a set of techniques is described to systematically analyze and report on objects found in SEM images. In particular, a reference template file (either aerial or GDSII) is used to compare design coordinate polygons to those extracted from a SEM image. An algorithm is explored that analyzes the target image and extracts objects based on heuristics that correspond to the SEM image type (or class). The algorithm contains various stages in which the image is filtered, conditioned and finally partitioned in order to extract objects. These objects are then, in turn, compared with the original template file. Information such as CD and threshold population distributions are collected and used as output. An example of OPC model validation using this technique is demonstrated and results from this analysis are presented. A prototypical template structure is simulated using an aerial imaging technique and is then compared to its corresponding SEM image. Finally, this same template image is compared with output of two GDSII-based simulations and discussed.