This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its applica- tion to continuous classi cation problems as a Nearest Prototype (NP) classi er. In Nearest Prototype classi ers, a collection of prototypes has to be found that accurately represents the input patterns. The classi er then assigns classes based on the nearest prototype in this collection. The MPSO algorithm is used to process training data to nd those prototypes. In the MPSO algorithm each particle in a swarm represents a single pro- totype in the solution and it uses modi ed movement rules with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with arti cial problems and with real benchmark problems and compared with several algorithms of the same family. Re- sults show that the particles are able to recognize clusters, nd decision boundaries and reach stable situations that also retain adaptation po- tential. The MPSO algorithm is able to improve the accuracy of 1-NN classi ers, obtains results comparable to the best among other classi ers, and improves the accuracy reported in literature for one of the problems.