In order to increase the performance of automatic sleep stage scoring, we propose a hybrid neural-network and rule-based expert system taking advantages of each system. The suggesting hybrid system comprises signal cleaning. feature extraction, event detection, rule-based sleep scoring and neural network classification. We selected segment based EEG features. the state of EOG. and EMG tone as a major feature set. With the extracted features, the rule-based expert system classities the sleep stages by symbolic reasoning. The scoring process of rule-based expert system comprises the single epoch reasoning based on the typical events and the multi-epoch adjusting when no events are detected. If the decision of rule-based expert system is uncertain, then these features are fed into the neural network. We used a two hidden layer feed forward network using error hack propagation algorithm. The agreement rate between human scorer and automatic algorithm were evaluated. The neural network supplements the shortcomings of rule-based system by dealing with exceptions of rules. The result shows that the compuational ol computational and symbolic intelligence is promising approach sleep signal anal) sis.