The problem of sequential change-point detection is concerned with the design and analysis of fastest ways to detect a change in the statistical profi le of a random time process, given a tolerable risk of a false detection. The subject finds applications, e.g., in quality and process control, anomaly and failure detection, surveillance and security, finance, intrusion detection, boundary tracking, etc. We provide an overview of the field's state-of-the-art. The overview spans over all major formulations of the underlying optimization problem, namely, Bayesian, generalized Bayesian, and minimax. We pay particular attention to the latest advances made in each. Also, we link the generalized Bayesian problem with multi-cyclic disorder detection in a stationary regime when the change occurs at a distant time horizon. We conclude with a case study to show the field's best detection procedures at work. This is joint work with Alexander G. Tartakovsky, Department of Mathematics and Center for Applied Mathematical Sciences, University of Southern California.