A better understanding of the behavior of individual grazing dairy cattle will assist in improving productivity and welfare. Global positioning systems applied to cows could provide a means of monitoring grazing herds while overcoming the substantial efforts required for manual observation. Any model of behavioral prediction using GPS needs to be accurate and robust by accounting for inter-cow variation as well as atmospheric effects. We evaluated the performance using a series of machine learning algorithms on GPS data collected from 40 pasture-based dairy cows over four months. A feature extraction step was performed on the collected raw GPS data, which resulted in 43 different attributes. Evaluated behaviors were grazing, resting and walking. Classifier learners were built using 10 times 10-fold cross-validation and tested on an independent test set. Results were evaluated using a variety of statistical significance tests across all parameters. We found that final model selection depended upon level of performance and model complexity. The classifier learner deemed most suitable for this particular problem was JRip; a rule-based learner (Classification accuracy = 0.85; False positive rate = 0.10; F-measure = 0.76; Area under the receiver operating curve = 0.87). This model will be used in further studies to assess the behavior and welfare of pasture-based dairy cows.