Knowing what an animal is doing where and when is crucial for understanding habitat use as well as for detecting deviations from the norm, e.g. the animal's responses to disturbances or predators. While an animal's position can quite easily be assigned using VHF- or GPS-telemetry, determining its behaviour from a distance is still limited. A new generation of GPS-collars, equipped with a dual-axis acceleration sensor allows insights into the animal activity by continuously (5-minute intervals) delivering x- and y-values on a scale from 0 to 255. However, until now it has not been possible to tell which activity values can be attributed to which kind of behaviour. Therefore, the overall aim of our study was to find a method to distinguish different behavioural categories from these activity values. We used four captive red deer Cervus elaphus (one male and three females) and equipped them with GPS-collars while simultaneously observing their behaviour. Values for different behavioural categories were compared statistically using ANOVA with ‘individual’ as random effect and Tukey's follow-up test. Threshold values between the categories were determined by recursive partitioning and were assured by 5,000 bootstraps. While the difference between feeding and slow locomotion was significant in the x- but not in the y-values, each of these two categories differed significantly from resting and fast locomotion. Specific thresholds were established between the three categories resting, feeding with slow locomotion and fast locomotion. Subsequent comparison of the behaviour determined by these threshold values with observed behaviour revealed a high percentage of correctly assigned behaviour (93%). Taken together, this preliminary study demonstrates the potential of dual-axis acceleration sensors in GPS-collars for estimating the activity of wild-living red deer. However, further observations of activity on more individuals of each age and sex class should be performed to take into account inter-individual variability and to improve the predictive power of the threshold values.