The sampling theorem is one of the most powerful results in signal analysis
. In this paper, we study the average sampling on shift invariant subspaces
, e.g. wavelet subspaces. We show that if a subspace satisfies certain cond
itions, then every function in the subspace is uniquely determined and can
be reconstructed by its local averages near certain sampling points. Exampl
es are given.