In this paper, a non-linear and non-normal filter using Monte Carlo simulation techniques is proposed, where the density function derived from the measurement equation and the random draws of the state-vector generated from the transition equation are utilized. The proposed filter has less computational burden and easier programming than the other non-linear and non-normal filters such as the numerical integration procedure and the Monte Carlo integration approach. Furthermore, the proposed filtei is extended to prediction and smoothing algorithms. Finally, by Monte Carlo experiments, wc compare the non-linear and non-normal procedures.