Bridge monitoring is a useful tool for alerting to changes in behavior and supporting informed bridge management decisions. However, the triggering of alerts is often based on predefined thresholds, which results in incomplete anomaly detection, leading to excessive false positives or negatives. This highlights the need for an efficient anomaly detection approach adapted to bridges. This paper presents a supervised classification framework for detecting and classifying anomalies in bridge monitoring data. The framework involves labelling data from existing datasets and training a classification algorithm to detect similar anomalies in new measurements. The focus is on strain measurements labelled using a predefined time series taxonomy, as illustrated by a case study of a bascule railway bridge. The framework demonstrates high accuracy in detecting and classifying anomalies, making it easy to identify their causes. It triggers alerts only when necessary and provides a reliable method for detecting changes in behavior.