Intelligent transportation systems (ITS) include large numbers of sensors that collect enormous quantities of traffic-flow data. The data provided by ITS are necessary for advanced forms of control. However, more basic forms of control, such as time-of-day (TOD) signal-timing plans that are widely used in the United States, do not directly rely on the data. Thus, sensor data often are unused and discarded. The sensor data, however, can provide large amounts of information that can aid in the development of improved TOD signal-timing plans. Data-mining tools are necessary to extract information from the data necessary to improve timing-plan development and to allow the timing-plan development and monitoring processes to be automated. A research effort that investigates the application of data-mining tools, including statistical clustering and classification techniques, to aid in the development of traffic-signal-timing plans is described. A case study was conducted that illustrated that hierarchical cluster analysis can be used to identify temporal interval breakpoints that support the design of a TOD signal-control system. The cluster analysis approach was able to use a high-resolution system state definition that takes full advantage of the extensive set of sensors deployed in a traffic signal system. Finally, the case study also demonstrated that a classification and regression tree could be developed that can be used to monitor automatically the quality of TOD intervals as traffic conditions change. The results of this research indicate that advanced data-mining techniques have great potential to provide automated techniques that assist traffic engineers in signal-control-system design and operations.