Pattern classification is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make reasonable decisions about the categories of the patterns. It is a very important in a variety of engineering and scientific disciplines such as computer vision, artificial intelligence, and medicine. New and emerging applications, such as Web searching, multimedia data retrieval, data mining, and machine learning require robust and efficient pattern classification techniques. Recently, functional network has been proposed as a generalization of the standard neural network. In This work we are interested in dealing with the statistical pattern recognition via functional networks and investigate its performance using some real-world applications. We use functional equations to approximate the neuron functions, which allow a wide class of functions to be presented. The steps of working with functional networks and the structural learning are proposed.