Parameters of the friction stir processing of mild steel plates were correlated with microhardness of the stir zone using artificial neural network (ANN) modeling and experimental methods. For this purpose, the number of passes, rotational speed, traverse speed, and addition of nano-sized Al2O3 powder were considered as input parameters for the ANN model, while microhardness of the center of the stir zone was obtained as the output. To examine the accuracy and capability of the model in predicting the microhardness, the effects of all ANN input parameters on the microhardness were also examined experimentally with and without the addition of Al2O3 nanopowder. For the surface nanocomposites produced, increase in the number of passes and rotation speed led to increased microhardness values, whereas higher traverse speed resulted firstly in a rise in microhardness followed by a decreased microhardness. Using optical and scanning electron microscopy, the variations in microhardness were closely discussed based on the microstructural changes. Experimental results proved to show excellent conformity with the ANN model.