Steady-state performance of a tracking filter is traditionally evaluated immediately after a track update. However, there is commonly a further delay (e.g., processing and communications latency) before the tracks can actually be used. We analyze the accuracy of extrapolated target tracks for four tracking filters: Kalman filter with the Singer maneuver model and worst-case correlation time, with piecewise constant white acceleration, and with continuous white acceleration, and the reduced state filter proposed by Mookerjee and Reifler. 1, 2 Performance evaluation of a tracking filter is significantly simplified by appropriate normalization. For the Kalman filter with the Singer maneuver model, the steady-state RMS error immediately after an update depends on only two dimensionless parameters. 3 By assuming a worst case value of target acceleration correlation time, we reduce this to a single parameter without significantly changing the filter performance (within a few percent for air tracking). 4 With this simplification, we find for all four filters that the RMS errors for the extrapolated state are functions of only two dimensionless parameters. We provide simple analytic approximations in each case.