Sequential (quickest) change-point detection is a branch of statistics concerned with the design and analysis of methods for rapid but reliable anomaly detection in ``live'' monitored random processes. The subject's areas of application are virtually unlimited, and include quality control, anomaly and failure detection, surveillance and security, finance, seismology, navigation, intrusion detection, boundary tracking---to name a few. This talk provides a brief overview of the field's state of the art. The overview includes a summary of the most popular probabilistic change-point models (minimax, Bayesian, generalized Bayesian) and a review of some of the ``mainstream'' change-point detection methods (CUSUM, Shiryaev's procedure and its variants). Certain open problems and challenges are discussed as well.