We report an experiment in which we use machine learning to validate the empirical objectivity of a novel annotation taxonomy for behavioral translation data. The HOF taxonomy defines three translation states according to which a human translator can be in a state of Orientation (O), Hesitation (H) or in a Flow state (F). We aim at validating the taxonomy based on a manually annotated dataset that consists of six English-Spanish translation sessions (approx 900 words) and 1813 HOF-annotated Activity Units (AUs). Two annotators annotated the data and obtain high average inter-annotator accuracy 0.76 (kappa 0.88). We train two classifiers, a Multi-layer Perceptron (MLP) and a Random Forest (RF) on the annotated data and tested on held-out data. The classifiers perform well on the annotated data and thus confirm the epistemological objectivity of the annotation taxonomy. Interestingly, inter-classifier accuracy scores are higher than between the two human annotators.