The remote IR sensor measurements and data reduction and analysis are important aspects of the development of massively parallel processing (MPP) skills. The problem of data fusion in a central decision center is very important in view of the deployment of distributed multiple sensors for communication, surveillance, and battle management. Because of a limited transmission capacity, the sensors are required to transmit their decision (with or without information bits) instead of the raw data. Besides, the performance of a sensor is based upon its operating conditions such as weather in the case of an infrared (IR) sensor. Several sensors can be used to increase recognition and classification of targets in general. If complementary sensors are used, then robust recognition can be achieved. An example of complementary sensors is IR and millimeter wave (MMW) sensors. The performance of an IR sensor (which has high resolution and day and night capability) decreases with inclement weather conditions whereas the performance of a MMW sensor (which suffers from low resolution) is not affected by these conditions. The basic goal of such a multiple-sensor distributed system is to improve system performance such as reliability, speed, coverage area, multiple target tracking, system response to various bands/channels for the various target or object features. This paper describes the processing of such information and suitable configurations to maximize applications.