The methodology of catchment extraction especially from regular grid digital elevation models (DEMs) is briefly reviewed. Then an efficient algorithm, which combines vector process and traditional neighbourhood raster process, is designed for extracting the catchments and subcatchments from depressionless DEMs. The catchment area of each river in the grid DEM data is identified and delineated, then is divided into subcatchments as required. Compared to traditional processes, this method for identifying catchments focuses on the boundaries instead of the area inside the catchments and avoids the boundary intersection phenomena. Last, the algorithm is tested with a set of DEMs of different sizes, and the result proves that the computation efficiency and accuracy are better than existent methods. The methodology of catchment extraction especially from regular grid digital elevation models (DEMs) is briefly reviewed. Then an efficient algorithm, which combines vector process and traditional neighborhood raster process, is designed for extracting catch and subcatchments from depressionless DEMs. The catchment area of each river in the grid DEM data is identified and delineated, then is divided into the subcatchments as required. the algorithm is tested with a set of DEMs of different sizes, and the result proves that the computation efficiency and accuracy are better than existent methods.