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The paper focuses on the development of an efficient and accurate tool for segmenting color images. The segmentation is a problem that has been widely studied since machine vision first evolved as a research area. The neural network segmentation tools and technology developed and presented in this paper show great potential in application where the accuracy is the major factor. Similar requirements exist in the area of medical imaging where segmentation must provide the highest possible precision. The feasibility of the work presented shows a promising future by using a cluster-based approach to training very large feedforward neural networks.