The sequential Monte Carlo (SMC) methodology recently emerged in the fields of statistics and engineering has shown a great promise in solving a large class of highly complex inference and optimization problems, opening up new frontiers for cross-fertilization between statistical science and many application areas.

SMC can be loosely defined as a family of techniques that use Monte Carlo simulations to solve on-line estimation and prediction problems in stochastic dynamic systems. By recursively generating random samples of the state variables, SMC adapts flexibly to the dynamics of the underlying stochastic systems. In this talk, we present an overview of the current status of SMC, its applications and some recent developments. Specifically, we will introduce a general framework of SMC, and discuss various strategies on fine-tuning the different components in the SMC algorithm, in order to achieve maximum efficiency. SMC applications, specially those in science, engineering, bioinformatics and financial data analysis will be discussed.