Effective tool life prediction holds important research value in that it can improve the machining efficiency and ensure the machining accuracy of a workpiece. However, accurate tool life prediction is difficult to achieve as it is influenced by many factors such as tool material, cutting parameters, and machining material. So we propose a method of tool life prediction based on a radial basis function (RBF) neural network optimized by the particle swarm optimization (PSO) algorithm. Firstly, the main parameters of the RBF neural network, namely the center value c, width σ, and connection weight w, are optimized by the PSO algorithm. Then, tool life prediction is carried out, with the factors affecting tool life as input neurons of the PSO-RBF neural network model and tool life as the output neuron. The experimental results show that the proposed method of tool life prediction based on the PSO-RBF neural network is feasible, with an average relative error reduced by 17.14% from that of the standard RBF neural network to 6.16%.