•A stochastic hybrid blade tip timing approach is proposed and demonstrated.•Turbomachine blade damage is identified and classified using this approach.•Probabilistic damage identification is achieved through natural frequency tracking.•Damage classification utilises a K-means clustering approach.•The proactive scheduling of a turbomachine outage is thus demonstrated. Blade Tip Timing (BTT) has been in existence for many decades as an attractive vibration based condition monitoring technique for turbomachine blades. The technique is non-intrusive and online monitoring is possible. For these reasons, BTT may be regarded as a feasible technique to track the condition of turbomachine blades, thus preventing unexpected and catastrophic failures. The processing of BTT data to find the associated vibration characteristics is however non-trivial. In addition, these vibration characteristics are difficult to validate, therefore resulting in great uncertainty of the reliability of BTT techniques. This article therefore proposes a hybrid approach comprising a stochastic Finite Element Model (FEM) based modal analysis and Bayesian Linear Regression (BLR) based BTT technique. The use of this stochastic hybrid approach is demonstrated for the identification and classification of turbomachine blade damage. For the purposes of this demonstration, discrete damage is incrementally introduced to a simplified test blade of an experimental rotor setup. The damage identification and classification processes are further used to determine whether a damage threshold has been reached, therefore providing sufficient evidence to schedule a turbomachine outage. It is shown that the proposed stochastic hybrid approach may offer many short- and long-term benefits for practical implementation.