A wide variety of concealed weapon detection systems are being investigated to determine the potential payoffs of employing these sensors to detect weapons concealed under a person's clothing. The enabling sensing mechanisms being studied include infrared, acoustic, millimeter wave, and X- ray sensors. The primary emphasis of this paper is on infrared. A new technique for processing sensor data by partitioning non-homogeneous images into homogeneous regions for later detection and identification processing is presented. The name of this method is Automated Statistical Characterization and Partitioning of Environments (A'SCAPE). A'SCAPE enables image enhancement for reliable detection and identification of weapons concealed under varying layers of clothing through its mapping process. By employing a variety of sensors, another enabling technology for concealed weapon detection (CWD) is sensor fusion. Concepts for experiments and analysis are discussed to determine the feasibility of sensor fusion approaches for CWD.