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Here, first we study the effectiveness of multilayer perceptron networks (MLPs) for prediction of the maximum and the minimum temperatures based on past observations on various atmospheric parameters. To capture the seasonality of atmospheric data, with a view to improving the prediction accuracy, we then propose a novel neural architecture that combines a self-organizing feature map (SOFM) and MLPs to realize a hybrid network named SOFM-MLP with better performance. We also demonstrate that the use of appropriate features such as temperature gradient can not only reduce the number of features drastically, but also can improve the prediction accuracy. These observations inspired us to use a feature selection MLP (FSMLP) instead of MLP, which can select good features online while learning the prediction task. FSMLP is used as a preprocessor to select good features. The combined use of FSMLP and SOFM-MLP results in a network system that uses only very few inputs but can produce good prediction.