This report presents several parallel implementations, on a MIMD machine, of a learning algorithm called OLS (Orthogonal Least Squares) for RBF (Radial Basis Function) neural networks. The sequential version is first described, and a straightforward parallel version is proposed. Two variants are developed, one of them reducing the complexity of the algorithm, and the other one improving the load balancing. An alternative is proposed for the storage of initial or intermediate data on local memory and discussed, according to the size of the application.