Sleep-disordered breathing is a disease that many people experience unconsciously and can have very serious consequences that can result in death. Therefore, it is extremely important to analyze the data obtained from the patient during sleep. It has become inevitable to use computer technologies in the diagnosis or treatment of many diseases in the medical field. Especially, advanced software using artificial intelligence methods in the diagnosis and decision-making processes of physicians is becoming increasingly widespread. In this study, we aimed to classify the sleep-disordered breathing type by using machine learning techniques utilizing time and time- frequency domain features. We used Pressure Flow, ECG, Pressure Snore, SpO2, Pulse and Thorax data from among the polysomnography records of 19 patients. We employed digital signal processing methods for six types of physiological data and obtained a total of 35 features using different feature extraction methods for five different classes (Normal, Hypopnea, Obstructive Apnea, Mixed Apnea, Central Apnea). Finally, we applied machine learning algorithms (Artificial Neural Network, Support Vector Machine, Random Forest, Naive Bayes, K Nearest Neighborhood, Decision Tree and Logistic Regression) on 5-class and 35-feature data sets. We used10 fold cross validation to verify the classification success. Our main contribution to the literature is that we developed a classification system to score all four different types of sleep-disordered breathing simultaneously by using 6 types of PSG data. As a five-class scoring result, the Random Forest (RF) algorithm showed the highest success with 76.3 % classification accuracy. When Hypopnea was excluded from the evaluation, classification accuracy increased to 86.6% for three Apnea-type disorders. Our proposed method provided 89.7% accuracy for the diagnosis of Obstructive Apnea by the RF classifier. The results show that time and time–frequency domain features are distinctive in Sleep-disordered breathing scoring, which is a very difficult process for physicians and a diagnostic support system can be design by evaluating many PSG data simultaneously.