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The demand of power and the size and complexity of the power system is increasing. Wide area monitoring and control is an integral part in transitioning from the traditional power system to a Smart Grid. However, wide area monitoring becomes challenging as the size of the electric power grid, and consequently the number of components to be monitored, grows. Wide area monitor (WAM) designed using feed-forward and feedback neural network architectures do not scale up to handle the growing complexity of the Smart Grid. In this paper, cellular neural network (CNN) is presented as a way to provide scalability in the development of a WAM for Smart Grid. The CNN based WAM is compared with multilayer perceptrons (MLP) based WAM on two different power systems. The results show that the CNN has better or comparable performance with, and scales up much better than, MLP.