In response to growing concerns over traffic congestion, traffic management systems have been built in large urban areas in an effort to improve the efficiency and safety of the transportation network. This research effort developed an automated condition monitoring method that uses archived traffic data to provide a basis for assessing current traffic conditions and, if applicable, determining the degree to which the conditions are abnormal. The method is an improvement over commonly used traffic condition monitoring methods in that the system state is characterized across a range of conditions rather than in an incident or incident-free condition. An additional improvement is that the interrelationships among traffic parameters are exploited by using multivariate statistical quality control (MSQC) rather than analyzing values of mean speed, volume, and occupancy (traffic variables typically measured in a traffic management system) independently. This statistical approach provides the tools and basis for the extensions needed to assess current traffic conditions using historical data. Prototype applications for use in traffic management systems and for data mining purposes were developed. These applications employ a newly developed procedure for screening both current and archived data from traffic detectors to reduce the potential of using erroneous data in the MSQC-based traffic condition monitoring method. Several strategies for sampling the historical database, based on temporal relationships with current data, were developed and evaluated. Implementation of the method will allow traffic managers to focus their efforts on abnormally operating locations and then determine an appropriate course of action to attempt to return the system state to normal. Potential benefits include more efficient use of human and computer resources in traffic management centers, improved return on investment in traffic detection infrastructure, and reduced traffic congestion.