Kullback information plays an important role in measuring the discrepancy between two probability density functions. In this paper, first, Kullback information is shown to be equivalent to the spectral error measure, which is developed to estimate the difference between two spectral densities. Then, to divide the non-stationary electroencephalogram data into stationary subsequences, segmentations of the data during sleep are carried out by using the spectral error measure. Next, to classify the segmented electroencephalographic data into one of the classes of the template patterns, Kullback information is also computed. Here, Kullback information is used as a measure of the distance between the template pattern and the segmented one. Finally, it is shown that sleep stages determined by the segmentation and the classification of the electroencephalogram data, are similar to those determined by a medical doctor.