Hypothesis-and-test (HAT) is the backbone of the model based paradigm. When the size and complexity of hypothesis space is non-trivial, iterative hypothesis refinement may be required before a satisfactory solution is achieved. In these cases the success of a model-based paradigm hinges on its ability to demonstrate strong convergence properties. When attempting to evaluate such iterative performance, it is useful to think of HAT as a two component process. The forward component maps hypothesis state variables to predicted features, ranging from pixel gray scale values to high level geometric properties of shape. The inverse component maps differences between predicted and observed features to changes in hypothesis state. Coarse-to-fine search strategies attempt to reduce the search domain by first applying features which behave smoothly over large regions in hypothesis space. Once the state of the search domain has been reduced, finer, more discriminating features are used. A feature characterization methodology which relates directly to the feature behavior irrespective of the particular predict-extract-match (PEM) algorithms employed would be implementation independent and hence, most general. Unfortunately, our ability to observe features is directly dependent on a feature extraction paradigm; our ability to accurately hypothesize underlying target state is dependent on a feature prediction paradigm; and our ability to compare between predicted and extracted features is dependent on a feature matching paradigm. Hence, we have little alternative but to adopt a methodology to characterize features in the context of a specific PEM configuration. This paper presents such a methodology using a novel lattice matcher paradigm. This new approach matches regions in hypothesis space against an extraction and produces a match surface instead of a single score or likelihood. This surface is then used to compute hypothesis state modifications. The extent over hypothesis space where the lattice matcher provides good hypothesis refinement is used to determine where in a search sequence a feature is best used. Characterization results are presented for peak, target and shadow features in simulated synthetic aperture radar data.