Distinguishing specific behavioural modes from data collected by animal-borne tri-axial accelerometers can be a time consuming and subjective process. Data synthesis can be further inhibited when the tri-axial acceleration data cannot be paired with the corresponding behavioural mode through direct observation. Here we explored the use of a tame surrogate (domestic dog) to build a behavioural classification module, and then used that module to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species. Tri-axial acceleration data were recorded from a domestic dog whilst it was commanded to walk, run, sit, stand, and lie-down. Through video synchronisation, each tri-axial acceleration sample was annotated with its associated behavioural mode; the feature vectors were extracted, and used to build the classification module through the application of support vector machines (SVM). This behavioural classification module was then used to identify and quantify the same behavioural modes in acceleration collected from a range of other species (alligator, badger, cheetah, dingo, echidna, kangaroo, and a wombat). Evaluation of the module performance, using a binary classification system, showed there was a high capacity (> 90 %) for behaviour recognition between individuals of the same species. Furthermore, a positive correlation existed between SVM capacity and the extent by which the individual had a spinal length-to-height above the ground ratio (SL:SH) similar to that of the surrogate. The study describes how to build a behavioural classification module and highlights the value of using a surrogate for studying cryptic, rare or endangered species.