Abstract: Only after the pest was identified, could the purposeful controlling of the pest be conducted in agricultural production. The identification of insects is one of the most important jobs in entomology. A novel method based on computer vision technology with a remote way can make a valuable contribution to reducing the burden of routine identifications. It can also provide a potential solution to the growing burden of routine species identifications presently faced by a dwindling community of expert taxonomists. Insect image was captured and uploaded to identification server from internet. Identification process was done by server automatically and results were given back to user immediately. Shape and color characteristics were used to form the separated pattern vector and RBF network was used as classifier for each pattern. Each classifier was linearly combined to give the final identification result. Shape pattern consists of rectangularity, elongation, roundness, sphericity, lobation and seven movement invariants. R, G, B, L histogram and R.G chromaticity histogram were treated as separated color characteristic vector. The identification system was used to test 16 species, 40 samples for each, with 20 of the 40 samples for “training” and the remainder for “test”. The accuracy reaches above 96%.