A recursive algorithm of stochastic approximation type is derived for the estimation of space-dependent parameters in a class of distributed parameter systems driven by random inputs. The algorithm uses noisy observations taken over at a finite number of discrete points located in the spatial domain. Stochastic convergence of the estimates is proven. Simulation studies illustrate the good convergence property of the algorithm. The algorithm is much more simple both in structure and implementation and less restrictive on system structure to apply, compared to the algorithm of Kubrusly and Curtain (1977)