A hand-held mine detector has two modes of operation: search and localization. In search mode, the goal is to identify areas where a buried mine might be located. Since minimizing the number of misses is a top priority, many regions identified in this mode may contain clutter. To separate the clutter from the mines, the detector can be switched into the localization mode during which a more thorough interrogation of the region is performed. Because causality is not required in localization mode, the analyzed signal is not limited to a single data point, but instead can consist of the response across an entire spatial "region". Previous work has demonstrated that so called "region processing" can potentially improve the localization performance of the detector. We have used the Minelab F1A4 metal detector, an EMI-based system, to collect regional data for a variety of objects including buried mines, metallic and non-metallic clutter, and short-circuited copper loops in free space. Several physics-based processing algorithms were developed and used to predict discrimination performance. Analysis of the loops, whose physical properties were known, indicated that discrimination between objects might be possible using a feature extracted from the detector output. Subsequently, this feature was used as the basis of an algorithm which, when used to process the mine/clutter data, significantly decreased the false alarm rate. This algorithm and its performance were further enhanced by incorporating information about the entire regional response of each object.