In order to better understand and describe surgical procedures by surgical process models, the field of workflow segmentation has recently emerged. It aims to recognize high-level surgical tasks in the Operating Room, with the help of sensors or human-based systems. Our approach focused on the automatic recognition of surgical phases by microscope images analysis. We used a hybrid method that combined Support Vector Machine and discrete Hidden Markov Model. We first performed features extraction and selection on surgical microscope frames to create an image database. SVMs were trained to extract surgical scene information, and then outputs were used as observations for training a discrete HMM. Our framework was tested on pituitary surgery, where six phases were identified by neurosurgeons. Cross-validation studies permitted to find a percentage of detected phases of 93% that will allow the use of the system in clinical applications such as post-operative videos indexation.