This paper describes an on-going effort to build a Multi- Agent Data Fusion Workstation (MADFW) based on a Knowledge- Based System (KBS) BlackBoard (BB) architecture to offer a range of innovative techniques for Data Fusion (DF), applicable to various domains. The initial application to be demonstrated is in the area of airborne maritime surveillance where several multi-agent concepts and algorithms have already been studied and demonstrated. The end result will offer the user a flexible and modular environment providing capability for: (1) addition of user defined sensor simulation models and fusion algorithms; (2) integration with existing models and algorithms; and (3) evaluation of performance to derive requirement specifications and help in the design phase towards fielding a real DF system. The workstation is being designed to accommodate modular interchangeable algorithm implementation and performance evaluation of: (1) fusion of positional data from imaging and non-imaging sensors; (2) fusion of attribute information obtained from imaging and non-imaging sensors and other sources such as communication systems, satellites, etc.; and (3) Object Recognition in imaging data. The design allows algorithms for sensor simulators and measures of performance to reside ether on the KBS BB shell or be separate from it, thus facilitating integration with other testbed designs. This architecture also allows the future introduction of fusion management capabilities. The real-time KBS BB shell developed by Lockheed Martin Canada, in collaboration with DREV, is the basis of the MADFW infrastructure. This system is totally generic, and could be used to implement any system comprising of components which can be numeric or AI based. It has been implemented in C++ rather than in a higher-level language (such as LISP, Smalltalk, ...) to satisfy the real-time requirement.