Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects the brain’s function. Electroencephalography (EEG) is a non-invasive technique that measures the electrical activity of the brain and can reveal its dynamics and information processing. We obtained the data during an eyes-opened resting state EEG recordings and it consisted of 52 children with autism (ASD) and 39 typically development (TD) children. In this study, we trained and compared 5 different classifiers for ASD and TD based on the EEG signals: the Dummy classifier, a Random Forest classifier, a K Nearest Neighbours classifier, a Support Vector Machine and an XGBoost classifier.