The emergence of new infectious diseases at a global scale as well as the threat of bioterrorism acts have highlighted the need for early detection of epidemics. In order to detect disease outbreaks as soon as possible, both real-time data collection and real-time interpretation of them are required. In the last decade, important advances in setting up techniques for real-time data collection has been noted. In addition statistical process control methods have been applied efficiently in order to detect an epidemic in a timely manner. In the present dissertation it first provides a short presentation of statistical process control techniques, then it describes the application of them methods in disease outbreak detection. Illustrative examples are also presented using either real or simulated public health data.