We consider the problem of locating jumps in regression surfaces. A ju
mp detection algorithm is suggested based on local least squares estim
ation, This method requires O(Nk) computations, where N is the sample
size and k is the window width of the neighborhood. This property make
s it possible to handle large data sets, The conditions imposed on the
jump location curves, the jump surfaces. and the noise are mild. We d
emonstrate this method in detail with some numerical examples.