Time series generated from NOAA operational satellite sensor temperature, noise, and calibration parameter statistics is a critical science analysis tool to trend instrument performance and detect and resolve on-orbit anomalies. Establishing this capability entails ingesting instrument engineering, housekeeping, and calibration data; performing statistics on them; and then storing and providing the resultant data and/or plots. For instruments with relatively small amounts of input and output data, this is a relatively easy task. For the NOAA Geostationary Operational Environmental Satellite R-Series Advanced Baseline Imager (ABI)—with three times more spectral information, four times the spatial resolution, and more than five times faster temporal coverage than previous GOES—instrument performance monitoring can be extremely complex because of the relatively large data volumes and number of parameters. Also of difficulty is that software and computing architecture needed to build such a system is usually proprietary and not openly documented. In order to fill this gap, we focus on the concept of operations and the results associated with the ABI instrument performance monitor. This monitoring system has proven to be extremely valuable in tracking instrument stability and detecting and performing initial diagnosis of ABI anomalies.