Complex ATR tasks are often decomposed into the identification of sub-targets, that is, objects are sorted and identified as one particular target type and then those targets are further identified. For instance, a field of view may be partitioned into natural and man-made objects. After which, the man-made objects are screened to identify a particular object of interest. These tasks combine classifiers which operate in isolation of each other, yet in fact, perform as a classification sequence. This work examines this scenario, building the ATR task as a sequence of target identifications. Two sequences will be highlighted: Believe the Negative (BN) and Believe the Extremes (BE). In a BN sequence, the second classification system only operates if a target is identified from the first classification system. In a BE sequence, the second classification system only operates if there is no identification from the first classification system. Performance of these classification sequences will be compared to classification systems operating separately. Further, sequence augmentation will be examined to demonstrate how the ATR task may still be completed when information is missing on the primary target. This missing information may represent atmospheric blurring, alternate field of view, or other disturbances. An example of the performance of the sequences under simulated, theoretical levels of missing information is examined, and formulas are presented to describe the optimal performance of these systems when augmented and un-augmented. In conclusion, this work demonstrates utility in how these sequences fuse target information in order to complete an ATR task.