Road/Traffic Color Image Segmentation is an important task for a variety of traffic applications. In the past few decades, many color image segmentation approaches have been developed based on different segmentation techniques, such as edge detection, multiple thresholds, region growing, space clustering, and spectral classification etc. However, due to the lack of adaptability for color fluctuation, current segmentation techniques have difficulty to overcome the additional effects by variable illuminations on color objects. Furthermore, learning skill for color features is not included in most color segmentation approaches. Hence, they have limited applications to road/traffic color image segmentation under a dynamic outdoor environment. In this paper, a new color image segmentation approach based on an improved Restricted Coulomb Energy (RCE) neural network is presented. It attempts to solve the problem of color classification in which color classes are represented by both disjoint class distributions and non-separable classes whose distributions overlap in color space. The color features of each color object are extracted by using the method of `The Estimation of Color Prototype Density' in RCE training procedure, they are stored in the prototype layer as color prototypes of a particulate color class. With the various prototypes established at the learning stage, RCE neural network is able to generate an optimal segmentation output in either the fast response mode or the output probability mode during color image segmentation.