Since the 1950s, numerous studies have been performed within the surveillance and reconnaissance (S&R) and target acquisition (TA) communities in an attempt to predict information extraction performance as a function of image collection and quality parameters. In general, the work followed two separate paths. The TA community developed models to predict probabilities of detection, recognition, and identification as a function of target size, range, and collection system design/performance parameters (e.g., MRT, FLIR92,NVTHERM,MRC). The S&R community developed models to predict National Imagery Interpretability Ratings (NIIRS) as a function of system design and collection parameters (e.g. IR GIQE). More recently, efforts have linked the two approaches such that NIIRS can be predicted from TA models and probabilities of identification can be predicted from NIIRS. With both approaches, resolution is a dominant term. A considerable amount of variability and uncertainty results from target differences. The criteria used to define the NIIRS generalize target type, size, and level of identification specificity. The TA predictions use the Johnson recognition criteria to relate lines on the target to recognition performance. A recent paper found that TA predictions differed substantially between the visible and IR. Further, the paper reported substantial differences among vehicles in terms of a confusion matrix. This finding was not surprising in light of other research, but suggested the need for a more detailed examination and explanation of results. Accordingly, the current effort was undertaken. Data from a variety of past studies dealing with target recognition were examined relative to the Johnson criteria, along with a more detailed analysis of data from two recent TA studies. A hypothesis of target recognition performance was generated and partially validated using available data.