Synoptic-scale cyclonic features provide an inescapable focal point for operational forecasting, whilst the merits of tracking such features are increasingly being recognized in the climate change field. Close association with adverse and extreme weather is the main motivator. Here a new and highly sophisticated set of techniques to detect, classify and track the full range is developed. A revised conceptual model of cyclone development provided the initial framework, ensuring a solid bond with forecasting practice, whilst also connecting closely to baroclinic life-cycle concepts. Building on this, cyclones are detected using a hybrid of geopotential minimum/vorticity maximum techniques, whilst incorporating important extensions to ensure that vorticity can be used at high resolution (∼50 km) and that features on fronts take priority. To track the features across time, at intervals of 12 h or less, feature attributes in the association process are used. Additionally, an upper-tropospheric steering wind is employed to estimate future and past positions. This facilitates 'half-time tracking', a new approach that has clear-cut advantages over 'full-time tracking' employed elsewhere.In detection tests, comparing with subjectively-drawn charts, the feature hit rate was 84%, and the false alarm ratio 17%, whilst in a simple tracking test the association failure rate was just 2%. These values compare very favourably with previous studies.One key application is discussed. This involves processing ensemble output to provide wide-ranging real-time products tailor-made to forecasters' needs. Products include track-following plume diagrams, for various cyclone attributes, and storm-track strike probability plots for different thresholds of severity.