Researchers have extensively explored uncertainty issues in Low Level Information Fusion (DFIG L0/L1 process levels) systems, and predominately use probabilistic uncertainty representations. However, this prominence does not happen in High-Level Information Fusion (HLIF) systems. One reason for this discrepancy is that HLIF systems ingest a wider range of evidence, with its associated uncertainties, and execute a broader scope of inferential reasoning than LLIF systems. Researchers developed multiple techniques to address these uncertainties and reasoning needs, but it is not clear when and where in a specific fusion system a particular technique should be applied. ISIF established the Evaluation of Technologies for Uncertainty Reasoning Working Group (ETURWG) to provide some clarity on this issue. As a first step, the ETURWG created the Uncertainty Representation and Reasoning Evaluation Framework (URREF). The framework formally represents concepts and criteria needed to evaluate the uncertainty management capabilities of HLIF systems. It provides 26 criteria for evaluating the effectiveness and resource efficiency of a fusion system’s uncertainty management capabilities. However, given the recency of the framework and the complexity of the issues it addresses, practitioners face difficulties in understanding where and how each criterion is applicable across a general fusion process environment, including a generic fusion system model. This paper’s primary contribution is to address this gap by providing a discussion of the significant application factors and considerations regarding the usage of the framework, while providing examples of such usage in the process.