Nearest Prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of proto- types has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collec- tion. In this paper we develop a new algorithm (called AMPSO), based on the Particle Swarm Optimization (PSO) algorithm, that can be used to find those prototypes. Each particle in a swarm represents a single proto- type in the solution; the swarm evolves using modified PSO equations with both particle competition and cooperation. Experimentation includes an artificial problem and six common application problems from the UCI data sets. The results show that the AMPSO algorithm is able to find so- lutions with a reduced number of prototypes that classify data with com- parable or better accuracy than the 1-NN classifier. The algorithm can also be compared or improves the results of many classical algorithms in each of those problems; and the results show that AMPSO also performs significantly better than any tested algorithm in one of the problems.