This paper contributes to modeling and supervision of multi-stage centrifugal compressors coping with real-gas processes and steady to highly transient operating conditions. A novel dynamic model is derived, and the incorporation of the generic LEE-KESLER-PLOCKER real-gas equation of state and its derivatives is presented. The model allows for embedding arbitrarily shaped performance maps, based on state-of-the-art polytropic change-of-state compressor characteristics. As the validity of these maps is a key issue for simulation and model-based monitoring, performance maps are treated as time-variant, and their shape is to be identified and monitored during operation. The proposed real-time map estimation scheme comprises an Unscented Kalman Filter and a newly proposed algorithm, referred to as Recursive Map Estimation. The combination yields a novel parameter and state estimator, which is expected to be superior if some parameters are characterized by a distinct operating point dependency. Two additional time-variant parameters are provided for monitoring: The first indicates the level of confidence in the local estimate, and the second points to drastic performance map alterations, which may be further exploited in fault detection. A modified reference simulation of a two-stage supercritical carbon dioxide compressor with known state trajectories, performance maps, and alterations demonstrates the successful application of the entire monitoring scheme, and serves for a discussion of the results. (C) 2019 Elsevier Ltd. All rights reserved.