Humaware have completed a feasibility study as part of RSSB's Future Railway Remote Condition Monitoring Competition (RCM) designed to meet the requirements of the challenge of `How to yield RCM alerts and alarms which are relevant, help ensure the right operational response happens in real time, and to use the data for predictive purposes to optimise safety and performance'. The objective of the project was to provide an emulation of the improvement in timeliness and relevance of alerts produced by Humaware's CFAR Autotrend adaptive anomaly detection tool and the ability to improve the accuracy and relevance of the operational responses. Network Rail selected the Track Circuit Indicator (TCI) as an exemplar for the feasibility study. The current technology used within Network Rail for detecting TCI defects works with five fixed thresholds that have to be individually set and maintained for each TCI. For this study, Humaware invoked their concept of Condition Indicators (CI), which identify features of the data to detect defects. Humaware developed the software to decompose the TCI data into CI data streams and then processed with CFAR Autotrend to identify level changes and trends. This was supported by a validation process, the Golden Database, which employed the Advanced Diagnostic Support tool based Bayesian process to provide validated diagnosis of the defect from the alerts.