Reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. It also decreases memory and transmission bandwidth requirements. Moreover, classification of image colors is applicable in image segmentation and object detection and separation, as well as producing pseudo-color images. In this paper, the Kohenen Self-Organizing Map Neural Network is employed to form an adaptive color reduction method. To enhance the performance of this method, we have used redundant features obtained by one-to-one functions from three main components of the color image (e.g. Red, Green and Blue channels). Exploiting these features will increase the color discrimination and details illustration ability of the network compared to the conventional approaches. This method leads to satisfactory results in image segmentation and especially in small object detection problems. It is also investigated that if the number of features in Kohenen network grows even by using non-deterministic one-to-one functions, the network revenue considerably improves. Moreover, we will study the effect of various adaptation algorithms in Kohenen network training stage. Again using a multi-stage color reduction procedure which employs both Kohenen neural networks and conventional vector quantization schemes improves the performance. Several experimental results are represented to simplify the comparison of different approaches.