A basic problem with augmented reality systems using head-mounted displays (HMDs) is the perceived latency or lag. This delay corresponds to the elapsed time between the moment when the user's head moves and the moment of displaying the corresponding virtual objects in the HMD. One way to eliminate or reduce the effects of system delays is to predict the future head locations. Actually, the most used filter to predict head motion is the extended Kalman filter (EKF). However, when dealing with nonlinear models (like head motion) in state equation and measurement relation and a non Gaussian noise assumption, the EKF method may lead to a non optimal solution. In this paper, we propose to use sequential Monte Carlo methods, also known as particle filters to predict head motion. These methods provide general solutions to many problems with any nonlinearities or distributions. Our purpose is to compare, both in simulation and in real task, the results obtained by particle filter with those given by EKF.