Children with cerebral palsy and complex communication needs face limitations in their access technology (AT) usage. Speech recognition software and conventional ATs (e.g., mechanical switches) can be insufficient for those with speech impairment and limited control of voluntary motion. Automatic recognition of head movements represents a promising pathway. Previous studies have shown the robustness of head pose estimation algorithms on adult participants, but further research is needed to use these methods with children. An algorithm for head movement recognition was implemented and evaluated on videos recorded in a naturalistic environment when children were playing a videogame. A face-tracking algorithm was used to detect the main facial landmarks. Head poses were then estimated using the Pose from Orthography and Scaling with Iterations (POSIT) algorithm and three head movements were classified through Hidden Markov Models (HMMs). Preliminary classification results obtained from the analysis of videos of five typically developing children showed an accuracy of up to 95.6% in predicting head movements.