A system and method for monitoring and reporting automated call-processing performance is disclosed. Initially, performance metrics can be established for normal or acceptable system performance. Performance metrics may be determined for individual components and/or a combination of components in a system, wherein the individual metric may help to provide a performance indicator for the call processing system. The performance metrics can be established by utilizing historical data gathered from past operating characteristics or operating parameters. The performance metric can be calculated from past operating characteristics and could be a statistical distribution or a statistical value. This statistical distribution will often conform to a Gaussian distribution also known as a bell curve. However, acceptable performance limits can be set based on nearly any statistical distribution. A monitor can collect data over a predetermined time interval and process the monitored data to provide a “near” real-time performance data. When the near real time performance data falls within a pre-defined range of normal operation (based on the statistical distribution) system operation is most likely acceptable and operation proceeds. However, when the system performance data is outside of a normal range of operation, beyond a performance metric or exceeds a threshold value an alarm can be set to notify a human or an electronic device.