The process of damage identification in bridge structures traditionally involves the extraction of one or more of the numerous modal-based Damage Sensitive Features (DSFs) from vibration data obtained by direct sensor measurement. However the performance of these modal-based DSFs can suffer under chang-ing environmental and operational conditions and are generally limited by their methodology, which assumes linear structural behaviour and signal stationarity. The present paper presents a detailed overview of the de-velopment of alternative DSFs derived from vibration characteristics, focusing on their conception, damage sensitivity evaluation and performance robustness assessment. Initially, selected vibration parameters are out-lined before their damage sensitivity is determined on progressive damage test data obtained from a post-tensioned three-span bridge under ambient conditions using supervised machine learning techniques in con-junction with the Minimum Covariance Determinate (MCD) estimator to mitigate uncertainty surrounding sources of excitation. Secondly, the performance robustness of the vibration-based DSFs is assessed on highly non-stationary data obtained from a progressively damaged steel truss bridge subjected to vehicle induced ex-citation. Finally, a comparative evaluation of the vibration-based DSFs is made against modal-based DSFs performance from the literature.