Micro-milling is widely used in material removal processes in industry. However, burrs are often formed on workpiece edges in milling process. Burr effects the dimensional tolerance and performance of the workpiece seriously and is desirable to be controlled. Burrs prediction technology is useful for cutting conditions optimization to control burrs forming. Due to lots of factors influencing the formation process of burr, it is a difficult task to establish the burr size prediction model by mathematical and mechanical method. RBF neural network was used for burr formation predition. Design of the network, network structure parameters determination and generalization capability of the network were analyzed and discussed. Achieved network has good fitting performance and generalization capability validated by experiments.