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Classification of a target is a key element of the Level 1 Fusion problem. Estimation of the classification of a potential target could be used to determine whether it should be prosecuted. This increasingly important problem requires the development of a quality estimate based on fusing reports across time and from a variety of sensors. The most common automated techniques for the classification problem provide a probability measure of the possible classes. Another concept in classification is the use of evidence accrual. As opposed to the creation of scoring techniques that use a random variable representation of the classification, the evidence accrual technique builds scores based on the information that can be compared to other scores or thresholds of the decision process. Since evidence affects the various potential classes differently, the technique developed is based on decoupled fuzzy-logic-based Kalman filters, similar to the concept of first-order observers. The proposed technique addresses three key issues in the classification problem. First, it is designed to incorporate both numeric and nonnumeric sensor reports. Second, it incorporates measurement uncertainty. Finally, it provides a level of uncertainty for each class. The technique is implemented in two forms: one that emulates the Bayesian taxonomy and one that allows for evidence to be independently applied to each potential class.