Combination of back propagation(BP)neural network and particle swarm optimization (PSO) algorithms is used to optimize process variables during the laser cladding. BP neural network model is developed to express the relationship between the clad process variables and the clad parameters (the width, height of clad bead), and the samples obtained in experiments are used to train network model to form the perfect map relation between input and output. Then, PSO algorithm is used to grabble the suitable values of the process variables. The experimental clad parameters with the process variable values calculated by this optimization method are coincident well with the expected ones. It is verified experimentally that combination of BP neural network and PSO algorithms can help to obtain the expected laser clad quality.