The integration of multi-scale digital maps has recently become an important task because of the popularity of web-GIS and map-related mobile platform. In order to produce different scales of maps from the vector data, numerous map generalization techniques have been developed to automatically generalize the vector data. An ideal generalization technique not only decreases the number of data points but also retains the similarity of the simplified shape to the original ones as close as possible. This paper proposed a new generalization approach; which uses the curvature and the shape distortion as the controlling factors to implement the task. The proposed method classifies the data points as the critical points and the secondary points. Since the critical points are the points that represent the distinctive features of the shape, therefore, if the generalization only accepts the critical points that may result in an over simplification. Hence, it's necessary to extract the secondary points to compensate the over-simplified situation. In this study, a Shape Distortion Index (SDI) is developed to detect the secondary points that can reduce the degree of the shape distortion efficiently. Two case studies are carried out to compare the proposed method with the commonly used Douglas-Peucker algorithm. The comparisons show that the proposed method has better simplified results and less shape distortion both perceptually and quantitatively than Douglas-Peucker method.