This paper presents a novel algorithm for multiobjective training of Radial Basis Function (RBF) networks based on least-squares and Particle Swarm Optimization methods. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem, in which two conflicting objectives should be minimized. The objectives are related to the empirical training error and the machine complexity. The training is done in three steps: i) a conventional minimization of the training error, ii) multiobjective least-squares optimization for the linear parameters and, iii) particle swarm optimization for the nonlinear parameters. Some results are presented and they show the effectiveness of the proposed approach.