Abstract: Change-Point problems have a variety of applications including industrial quality control, reliability, fault detection, surveillance, and security systems. By monitoring data streams which are generated from a process, we are interested in quickly detecting malfunctioning once the process goes out control, while keeping false alarms as infrequent as possible when the process is in control. The classical or centralized version of this problem, where all observations are available at a single, central location, is a well-developed area. In this talk, we investigate the decentralized version where the information available is distributed across a set of sensors. Each sensor receives a sequence of observations, and sends a sequence of sensor messages to a central processor, called the fusion center, which makes a final decision when observation are stopped. In order to reduce the communication costs, it is required that the sensor messages belong to a finite alphabet. In the decentralized change-point problem, the goal is to detect the change as soon as possible over all possible protocols for generating sensor messages and over all possible decision rules at the fusion center, under a restriction on the frequency of false alarms. We will present a general asymptotic theory, and provide procedures that are asymptotically optimal and easy to implement.