Mine Hunter/Killer (MH/K) is an Advanced Technology Demonstration (ATD) program directed by the Army Night Vision Electronic Sensors Directorate (NVESD). The MH/K system consists of a vehicle-mounted system that detects and neutralizes surface and buried anti-tank (AT) mines. The detection element in this program consists of a Close-In Detection (CID) System that relies on a multi-sensor configuration. The CID System consists of three sensors: a ground penetrating radar (GPR), a metal detector (MD) and a forward-looking IR imaging system. TRW S and ITG has provided support for analysis, testing and algorithm development for Automatic Target Recognition and sensor fusion processing. This paper presents a multi-sensor fusion approach developeby TRW under this effort. In this approach, the incoming alarms from the three sensors are segregate into five classes, based on spatial coincidence of GPR and MD alarms, and on the presence of a surface null in the GPR depth profile. This GPR null, or 'notch', is indicative of shallowly buried objects or clutter, and helps in the discrimination against false alarm density, attempting to maintain a constant false alarm rate. This paper will describe this fusion methodology and the adaptive threshold method in detail, show the target and clutter probability density functions for each class, and show result form recent field test. Fused results will be compared with single sensor performance, and strengths and weaknesses of each sensor will be discussed.