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Neural networks are known for their ability to learn and classify patterns based on certain criteria defined within the training process. Fuzzy ARTMAP neural networks are examples of such systems where the output is decided based on the input/output pattern training scheme. In this research, we build a fuzzy ARTMAP like neural network that depends on an adaptive Euclidian distance neighborhood rather than the fuzzy AND neighborhood in deciding the network output. It is a supervised input/output clustering algorithm that calculates the Euclidean distance between input patterns and system stored categories (neurons) to determine the corresponding output even when that input pattern has never been seen. Euclidean ARTMAP neural network or better known as EARTMAP neural network is trained according to a certain algorithm that calculates the Euclidean distance and decides to whether include the new pattern in an already existing category (cluster) and update its position in the clustering map, or to consider it as a new category if it is far enough from all of the existing categories. The new location of a cluster center is found by averaging the location of all of the patterns that belong to the cluster itself. This would help in suppressing the white noise level that accompanies those patterns during training. The above mentioned algorithm is tested in a control experiment and worked as a human like system to track a moving target in the plane. The importance of EARTMAP neural network is its ability to imitate certain systems to give a performance that is close to the original performance with a minimum number of categories.