Lay error is a primary source of error in fire control, which is defined as “the gunner’s inability to lay the sight crosshairs exactly on the center of the target.” To evaluate the potential implementation of computer vision and artificial intelligence algorithms for improving gunners’ performance or enabling autonomous targeting, it is crucial for the US Army to establish a benchmark of human performance as a reference point. In this study, we present preliminary results of a human subject study conducted to establish such a baseline. Using the Unreal Engine [1], we developed a photorealistic simulation environment with various targets. Fifteen individuals meeting the military applicant criteria in terms of age were assigned the task of aligning crosshairs on targets at multiple ranges and under different motion conditions. Each participant fired at 240 targets, resulting in a total of 3600 shots fired. We collected and analyzed data including lay error and time to fire. The initial analysis reveals that subjects demonstrated a significant number of outliers in lay error, and there was notable variation between subjects.