Within the context of our sensor fusion systems, we define an entity's vulnerability as the certainty with which other entities have the capability to detect and/or strike the entity; vulnerability assessment (VA) is the inference of vulnerability certainties. This investigation considers two issues: the feasibility of a fuzzy VA algorithm and the interface of a fuzzy VA algorithm into an existing sensor fusion system, including human-machine interface aspects. Relative kinematics, sensor/weapon technical capabilities, sensor/weapon system state, contextual electronic signatures, physics, terrain, atmospherics, and doctrinal bias are certainly all viable inputs to a VA algorithm. These data are traditionally characterized by a mix of continuous, discrete, and/or symbolic values with associated error bounds in various mathematical forms. Hence, the algorithmic infusion of a fuzzy VA into this systemic environment implies resolving the uncertainty information content of these representations and integrating them into a coherent fuzzy reasoning context. The information overload facing the tactical operator has necessitated the reduction of many data to prioritized simple alerts. While there is a reasonable understanding of the visual representations and implications of thresholding probabilistic data, the presentation and thresholding of fuzzy data is not well understood; some of the more critical implications on the human-machine interface are presented herein.