1. The development of multi-sensor animal-attached tags, recording data at high frequencies, has enormous potential in allowing us to define animal behaviour. 2. The high volumes of data, are pushing us towards machine-learning as a powerful option for distilling out behaviours. However, with increasing parallel lines of data, systems become more likely to become processor limited and thereby take appreciable amounts of time to resolve behaviours. 3. We suggest a Boolean approach whereby critical changes in recorded parameters are used as sequential templates with defined flexibility (in both time and degree) to determine individual behavioural elements within a behavioural sequence that, together, makes up a single, defined behaviour. 4. We tested this approach, and compared it to a suite of other behavioural identification methods, on a number of behaviours from tag-equipped animals; sheep grazing, penguins walking, cheetah stalking prey and condors thermalling. 5. Overall behaviour recognition using our new approach was better than most other methods due to; (i) its ability to deal with behavioural variation and (ii) the speed with which the task was completed because extraneous data are avoided in the process. 6. We suggest that this approach is a promising way forward in an increasingly data-rich environment and that workers sharing algorithms can provide a powerful library for the benefit of all involved in such work.