Sensor and data fusion are exploited in diverse applications such as Earth resource monitoring, weather forecasting, vehicular traffic management, and target classification and state estimation. The approach used in this chapter to describe data fusion and its objectives is based on a model developed for the U.S. Department of Defense. The model divides data fusion into low-level and high-level processes. Low-level processes support preprocessing of data and target detection, classification, identification, and state estimation. High-level processes support situation and impact refinement and fusion process refinement. The duality between the data fusion and resource management models of processing levels can lead to improved insight into and utilization of resource management assets. Various categories of algorithms are available to implement target detection, classification, and state-estimation fusion. In addition, several data fusion architectures exist for combining sensor data in support of data fusion. The architectures are differentiated by the amount of processing applied to the sensor data before transmission to the fusion process, resolution of the data that are combined, and the location of the data fusion process. The chapter concludes by addressing several concerns associated with the fusion of multi-sensor data. These encompass dissimilar sensor footprint sizes, sensor design and operational constraints that affect data registration, transformation of measurements from one coordinate system into another, and uncertainty in the location of the sensors.