The aim of this study was to develop a fully automatic sleep scoring algorithm on the basis of a reproduction of new international sleep scoring criteria from the American Academy of Sleep Medicine. A biomedical signal processing algorithm was developed, allowing for automatic sleep depth quantification of routine polysomnographic recordings through feature extraction, supervised probabilistic Bayesian classification, and heuristic rule-based smoothing. The performance of the algorithm was tested using 28 manually classified day-night polysomnograms from 18 normal subjects and 10 patients with Parkinson disease or multiple system atrophy. This led to quantification of automatic versus manual epoch-by-epoch agreement rates for both normals and abnormals. Resulting average agreement rates were 87.7% (Cohen's Kappa: 0.79) and 68.2% (Cohen's Kappa: 0.26) in the normal and abnormal group, respectively. Based on an observed reliability of the manual scorer of 92.5% (Cohen's Kappa: 0.87) in the normal group and 85.3% (Cohen's Kappa: 0.73) in the abnormal group, this study concluded that although the developed algorithm was capable of scoring normal sleep with an accuracy around the manual interscorer reliability, it failed in accurately scoring abnormal sleep as encountered for the Parkinson disease/multiple system atrophy patients.