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As capabilities of autonomous systems expand, traditional geospatial information displays used for supervisory control will likely need to be augmented with more explicit information on higherorder autonomous activities, such as goal-directed task selection, situation assessment, decisionmaking, and planning. We present a supervisory control interface for higher-order autonomy based on finite state machine diagrams called Layered Pattern Recognizable Interfaces for State Machines (L-PRISM). L-PRISM is a hierarchically arranged set of nested state diagrams coupled with a temporal control and payload viewer. The mission goals and tasks are at the top-most layer and sub-tasks and states at lower layers, providing varying levels of information abstraction. Task diagrams use unique layouts to facilitate pattern recognition, which is anticipated to improve the operator’s situation assessment. Furthermore, system behaviors can be viewed in real time or retrospectively evaluated. This paper will describe the L-PRISM concept and plans for examining its effect on supervisory control performance.