The authors investigate the parameter identification problem for distributed parameter systems. The problem of parameter identification in distributed parameter systems from noisy data is both nonlinear and ill- posed. They develop the concept of regularization, which is widely used in solving linear Fredholm integral equations, for the identification of parameters in distributed parameter systems. They first present a general regularization identification theory and then apply it to the identification of parabolic systems. The performance of the regularization identification method is evaluated by numerical experiments on the identification of a spatially varying diffusivity in the diffusion equation.