Studies have shown that patients suffering from sleep deprivation have a risk for hypertension, diabetes and depression that is higher than normal sleepers. Treatment for all these problems requires accurate analysis of the sleep stages and patterns in the polysomnographic signals collected in overnight recording over several months. However, manual sleep staging is a repetitive and time-consuming process as marking one typical eight hours overnight polysomnographic recording can take up to two hours to complete. Due to increased processing capabilities, it is now possible to automate this process and assist the sleep expert. A large number of algorithms have been proposed during the last few decades. This review article presents an overview of the existing automatic sleep staging methods, discusses the different challenges and proposes future prospects for new research opportunities.