The need for accurate monitoring and analysis of sequential data arises in many scientic, industrialand nancial problems. Although the Kalman lter is effective in the linear-Gaussiancase, new methods of dealing with sequential data are required with non-standard models.Recently, there has been renewed interest in simulation-based techniques. The basic idea behindthese techniques is that the current state of knowledge is encapsulated in a representativesample from the appropriate posterior distribution. As time goes on, the sample evolves andadapts recursively in accordance with newly acquired data. We give a critical review of recentdevelopments, by reference to oil well monitoring, ion channel monitoring and trackingproblems, and propose some alternative algorithms that avoid the weaknesses of the currentmethods.