Now that advanced aircraft are committed to using Helmet Mounted Displays (HMDs) in combat operations, major issues associated with their use in simulators must be addressed. These issues manifest themselves through physiological disturbances similar to symptoms of simulator sickness and include eyestrain, headache, nausea, sweating, dizziness, and a general sensation of not feeling well. Slow update rates and long lag times have been implicated as contributing to simulator sickness. Additionally, simulator sickness can be a significant distraction during training and may result in ineffective training, negative training, reduced user acceptance, and a reduction in simulator usage. Innovative solutions to address latency problems must be developed so that training can be optimized as aircrews are afforded the capability to train as they fly using HMDs in a simulation environment. Typical Kalman predictive filter algorithms have been applied to the problem of latency mitigation with some limited success since the early 1970's. The approach discussed here examined a customized Kalman predictive filter and a neural network approach. This strategy implemented in this research is to combine two predictions based on past and current head motion data. Recent data collected indicates that the Kalman predictive filter/Neural Net approach produced enhanced prediction capability and greatly reduced total system latency through more accurate predictions of head/neck movement. The current study compared a typical linear extrapolation prediction to the customized Kalman solution and a Neural Net solution developed for this effort. Results indicate a 50% reduction in magnitude of error produced and eight times fewer large errors produced. Reduced latency should ease some simulator sickness symptoms. Implications for training systems and improved training will be discussed.