Understanding process models involves different cognitive processes. These processes typically manifest in users’ visual behavior and thus can be captured using eye-tracking. In this paper, we focus on the detection of two very essential behaviors: information search and inference. Using a set of eye-tracking features allowing to discern these two behaviors, we train several machine learning (ML) models to predict whether the user is involved in a search phase or an inference one. Following a cross-validation approach inspired by the leave-one-out method, our ML models attain 85% precision, 82% recall, and an F1 score of 80%. The outcome of this work enables the creation of novel adaptive systems, detecting whether the user is involved in a search or inference phase and accordingly providing adequate support. Moreover, it opens up new opportunities to better understand how different process model, tool, user and task-related factors affect users’ search and inference behaviors.