Data fusion algorithms must typically address not only kinematic issues—that is, target tracking—but also nonkinematics—for example, target identification, threat estimation, intent assessment, etc. Whereas kinematics involves traditional measurements such as radar detections, nonkinematics typically involves nontraditional measurements such as quantized data, attributes, features, natural-language statements, and inference rules. The kinematic vs. nonkinematic chasm is often bridged by grafting some expert-system approach (fuzzy logic, Dempster-Shafer, rule-based inference) into a single- or multi-hypothesis multitarget tracking algorithm, using ad hoc methods. The purpose of this paper is to show that conventional measurementto-track association theory can be directly extended to nontraditional measurements in a Bayesian manner. Concepts such as association likelihood, association distance, hypothesis probability, and global nearestneighbor distance are defined, and explicit formulas are derived for specific kinds of nontraditional evidence.