Where, when and how much animals eat provide valuable insights into their ecology. In this paper, we present a comparative analysis between Support Vector Machine (SVM) and Input Delay Neural Network (IDNN) models to identify prey capture events from penguin accelerometry data. A pre-classified dataset of 3D time-series data from back-mounted accelerometers was used. We trained both the models to classify the penguins’ behavior at intervals as either ‘prey handling’ or ‘swimming’. The aim was to determine whether IDNN could achieve the same level of classification accuracy as SVM, but with reduced memory demands. This would enable the IDNN model to be embedded on the accelerometer micro-system itself, and hence reduce the magnitude of the output data to be uploaded. Based on the classification results, this paper provides an analysis of the two models from both an accuracy and applicability point of view. The experimental results show that both models achieve an equivalent accuracy of approx. 85% using the featured data, with a memory demand of 0.5 kB for IDNN and 0.7 Mb for SVM. The raw accelerometer data let us improve the generalizability of the models with a slightly lower accuracy to around 80%. This indicates that the IDNN model can embed on the accelerometer itself, reducing problems associated with raw time-series data retrieval and loss.