Distributed data fusion architectures and algorithms are of particular interest in the design of surveillance and monitoring systems that are used for incident detection, situation monitoring, and security management functions. The fusion details in these systems are often predicated by the types of sensors chosen for deployment and their characteristics (which may include sensor cost, robustness, redundancy, physical size limitations, and the processing requirements/limitations they may introduce). For instance, a typical surveillance and monitoring scenario might include a multitude of rather inexpensive sensors that are randomly distributed over the geographical area under surveillance, and might include several vibration sensors (that detect seismic activity), acoustic sensors, RF energy sensors (using omni-directional antennae), in addition to conventional imaging sensors. Individually these sensors may not enjoy a high degree of reliability or possess very extensive performance capabilities; however, when deployed in a large number they can form a distributed sensor network (even forming an ad hoc network) capable of collectively providing a fused performance that fulfills the objectives of the specific application. Fusion architectures and algorithms for such applications require novel concepts and methodologies that are significantly different from available methods (most of which were developed during the past decade in the context of military applications involving a handful of expensive and high capability sensors such as radar systems). In this article, we shall describe our recent work in the design of such architectures and algorithms focusing on the concept of “information value maps.” In assessing a fused sensor system, one considers the quality of the system architecture most often by the capabilities of the individual sensors and the attributes of the fusion algorithm. Though it is possible to evaluate system performance in an idealized context and model real world perturbations as random disturbances, it may be advantageous to treat predictable events as deterministic. Additionally, the a priori information one may have need not be limited to constraints on the objects of interest but also can be applied to constraints on the scene in which the object resides. In general, the environment scanned by a set of real world sensors is not homogeneous with regard to sensor performance. The qualitative effect of the non-homogeneous environment could be quantified through assignment of a value, termed information value (IV), that corresponds to the amount of trust associated with a sensor's measurement when observing a particular location and further to describe information value maps, which provide a graphical representation of ordered collection of IV assignments. A few general guidelines for the development of information value maps in surveillance and monitoring applications will be outlined in this article.