A bird’s–eye overview of the innovative, on–board and Multi–Purpose, random vibration based MAIANDROS Condition Monitoring system for railway vehicles and infrastructure is presented. The system includes Modules for Suspension Monitoring (SM), Wheel Monitoring (WM), Track Monitoring (TM) for track segment condition characterization, Lateral Stability Monitoring (LSM), and Remaining Useful Life Estimation (RULE) for critical components such as wheels. It is based on Statistical Time Series type methods and proper decision making, and aims at overcoming various challenges of current systems while pushing their performance limits. Its unique advantages include high diagnostic performance, ability to detect early–stage (incipient) faults, robustness to varying Operating Conditions, early detection of the onset of hunting, operation with a minimal number of low–cost sensors, and minimal computational complexity for achieving real–time or almost real–time operation. Its high achievable performance is demonstrated via indicative assessments using a prototype system onboard an Athens Metro vehicle and Monte Carlo simulations with a SIMPACK based high–fidelity vehicle model.