Cataract　is　one　of　the　most　common　diseases　that　might　cause　blindness.　Previous　research　shows　that　cataract　occupies　almost　50%　in　severe　visually　impairments.　Considering　the　fact　that　retinal　image　is　one　of　the　most　important　medical　references　that　help　to　diagnose　the　cataract,　this　paper　proposes　to　use　a　neural　network　classifier　for　automatic　cataract　detection　based　on　the　classification　of　retinal　images.　The　classifier　building　procedure　includes　three　parts:　preprocessing,　feature　extraction,　and　classifier　construction.　In　the　pre-processing　part,　an　improved　Top-bottom　hat　transformation　is　proposed　to　enhance　the　contrast　between　the　foreground　and　the　object,　and　a　trilateral　filter　is　used　to　decrease　the　noise　in　the　image.　According　to　the　analysis　of　pre-processed　image,　the　luminance　and　texture　message　of　the　image　are　extracted　as　classification　features.　The　classifier　is　constructed　by　back　propagation　(BP)　neural　network　which　has　two　layers.　Based　on　the　clearness　degree　of　the　retinal　image,　the　patients＇　cataracts　are　classified　into　normal,　mild,　medium　or　severe　ones.　The　initial　evaluation　results　illustrate　the　effectiveness　of　our　proposed　approach,　which　has　great　potential　to　improve　diagnosis　efficiency　of　the　ophthalmologist　and　reduce　the　physical　and　economic　burden　of　the　patients　and　society.　©　2013　IEEE.