A　cataract　is　the　prevailing　cause　of　visual　impairment　in　the　modern　world.　The　detection　of　cataract　at　early　stages　can　lessen　the　risk　of　blindness.　This　study　presents　an　automated　system　for　cataract　detection　and　grading　based　on　retinal　images.　The　system　is　comprised　of　image　acquisition,　preprocessing,　feature　extraction,　classifier　building,　and　cataract　detection　and　grading.　The　preprocessing　steps　such　as　green　channel　extraction,　histogram　equalization,　and　top-bottom　hat　transformation　are　used　to　improve　the　quality　of　retinal　images.　The　wavelet　and　texture　features　are　extracted　from　the　fundus　image　for　building　a　classifier.　A　combination　of　SOM　(Self-Organizing　Maps)　and　RBF　(Radial　Basis　Function)　neural　network　has　been　taken　to　obtain　better　prediction　accuracy　of　cataract.　SOM-RBF　neural　network　is　evaluated　on　Tongren　dataset　with　8030　subjects　categorized　into　four　classes:　Normal,　Mild,　Mature,　and　Severe.　The　proposed　method　achieved　95.3%　and　91.7%　of　accuracy　for　cataract　detection　and　grading　tasks,　respectively.　The　experimental　results　indicate　that　the　proposed　method　performs　better　than　the　traditional　RBF　and　other　baseline　methods.