EMI sensors are used extensively to detect landmines, and operate by detecting the metal that is present in mines. However, mines vary in their construction from metal-cased varieties with a large mass of metal to plastic-cased varieties with minute amounts of metal. Unfortunately, there is often a significant amount of metallic clutter present in the environment. Consequently, EMI sensors that utilize traditional detection algorithms based solely on metal content suffer from large false alarm rates. We have at least partially mitigated this false alarm problem for high-metal content mines by developing statistical algorithms that exploit phenomenological models of the underlying physics. The Joint UXO Coordination Office (JUXOCO) is sponsoring a series of experiments designed to establish a performance baseline for a variety of sensors. The experiments to date have focused on detection and discrimination of low-metallic content mines. This baseline will be used to measure the potential improvements in performance offered by advanced signal processing algorithms. This paper describes the results of several experiments performed in conjunction with the JUXOCO effort. In our preliminary work, statistical algorithms have been applied specifically to the problem of detection of low-metal mines, and dramatic performance improvements have been observed with respect to the baseline performance. However, these algorithms were statistical in nature, did not incorporate phenomenological models, and exploited spatial information. The tradeoffs among these various factors are explored in this paper, along with the performance of alternative statistical approaches. In addition, approaches to classification of the mine-type are discussed and the performance of such classifiers is presented.