A new layered graph network (LGN) for image segmentation is presented. In the LGN a graph representation of images is used. In such a pixel adjacency graph (PAG) a segment is considered as a connected component. To define the PAG the layers of the network are divided into regions, and inside the regions the image is represented by sub-graphs consisting of sub-segments (nodes) which are connected by branches if they are adjacent. The connection of sub-segments is controlled by a special adjacency criterion which depends on the mean gray values of the sub-segments and their standard deviations. This way, the sub-segments of a layer 1 are merges of sub-segments of layer 1-1 (the sub-segments of layer 0 are the pixels). The gray value averaging over the sub-segments is edge preserving and becomes more and more global with the increasing number of the network layer. Bridge connections between the segments are prevented by the special regional structure of the network layers. The LGN can be understood as a special 'neural' vision network with the highest layer representing the PAG. Simulated and real world images have been processed by a LGN simulator with good success.