The application of Monte Carlo techniques to Bayesian state estimation is discussed. A simple theory for the Monte Carlo uncertainty is developed showing that the number of Monte Carlo replications does not in principle have to be large. A recursive on-line algorithm based on rejection sampling is given and improved versions suggested. The methods are illustrated on a non-linear pendulum with measurement saturation.