Objective The implementation of a computer assisted system for real-time classification of the electroencephalogram (EEG) in critically ill patients. Methods Eight quantitative features were extracted from the raw EEG and combined into a single classifier. The system was trained with 41 EEG recordings and subsequently evaluated using an additional 20 recordings. Through visual analysis, each recording was assigned to one of the following categories: normal, iso-electric, low voltage, burst suppression, slowing, and EEGs with generalized periodic discharges or seizure activity. Results 36 (88%) recordings from the training set and 17 (85%) recordings from the test set were classified correctly. A user interface was developed to present both trend-curves and a diagnostic output in text form. Implementation in a dedicated EEG monitor allowed real-time analysis in the intensive care unit (ICU) during pilot measurements in four patients. Conclusions We present the first results from a computer assisted EEG interpretation system, based on a combination of eight quantitative features. Our system provided an initial, reasonably accurate interpretation by non-experts of the most common EEG patterns observed in neurological patients in the adult ICU. Significance Computer assisted EEG monitoring may improve early detection of seizure activity and ischemia in critically ill patients.