In　order　to　recognise　and　filter　pornographic　images,　visual-word-based　image　representation　has　attracted　more　and　more　attention.　An　image　can　be　represented　as　a　bag　of　visual　words,　which　is　analogous　to　the　bag-of-words　representation　of　text　documents.　However,　most　of　the　existing　approaches　create　visual　words　from　images　in　the　pixel　domain,　which　requires　extra　processing　time　to　decompress　images,　since　most　images　are　stored　in　compressed　formats.　A　novel　pornographic　images　recognition　method　based　on　visual　words　in　a　compressed　domain　is　proposed　in　this　study.　There　are　four　steps　in　this　method:　(i)　low-resolution　image　is　constructed　from　compressed　data;　(ii)　scale-invariant　feature　transform　(SIFT)　descriptors　are　extracted　from　this　low-resolution　image;　(iii)　a　visual　vocabulary　is　created　based　on　SIFT　descriptors;　(iv)　pornographic　images　are　identified　by　using　a　support　vector　machine　(SVM)　classifier.　The　experimental　results　indicate　that　the　proposed　method　can　recognise　pornographic　images　accurately　with　much　less　computational　time.