This paper describes a case study of model-based diagnostics system development for an aircraft auxiliary power unit (APU) turbine system. The off-line diagnostics algorithms described in the paper work with historical data of a flight cycle. The diagnostics algorithms use detailed turbine engine systems models and fault model knowledge available to an engine manufacturer. The developed algorithms provide fault condition estimates and allow for consistent detection of incipient performance faults and abnormal conditions.