Obstructive Sleep Apnea Syndrome (OSAS) is defined as a sleep related breathing disorder that causes the body to stop breathing for about 10 seconds and mostly ends with a loud sound due to the opening of the airway. OSAS is traditionally diagnosed using polysomnography, which requires a whole night stay at the sleep laboratory of a hospital, with multiple electrodes attached to the patient's body. Snoring is a symptom which may indicate the presence of OSAS; thus investigation of snoring sounds, which can be recorded in the patient's own sleeping environment, has become popular in recent years to diagnose OSAS. In this study, we aim to develop a new method to detect post-apnea snoring episodes with the goal of diagnosing apnea or creating new criteria similar to apnea / hypopnea index. Emphasis is placed on detecting post apnea episodes, hence the apnea periods. In this method, first segmentation is done to eliminate the silence parts. Then, these episodes are represented by distinctive features; some of these features are available in literature but some of them are novel. Finally, episodes are classified using supervised methods. False alarm rates are reduced by adding additional constraints into the detection algorithm. These methods are applied to snoring sound signals of OSAS patients, recorded in Gulhane Military Medical Academy, to verify the success of our algorithms.