Low-rank　representation　(LRR)　has　recently　attracted　great　interest　due　to　its　pleasing　efficacy　in　exploring　low-dimensional　subspace　structures　embedded　in　data.　One　of　its　successful　applications　is　subspace　clustering　which　means　data　are　clustered　according　to　the　subspaces　they　belong　to.　In　this　paper,　at　a　higher　level,　we　intend　to　cluster　subspaces　into　classes　of　subspaces.　This　is　naturally　described　as　a　clustering　problem　on　Grassmann　manifold.　The　novelty　of　this　paper　is　to　generalize　LRR　on　Euclidean　space　into　the　LRR　model　on　Grassmann　manifold.　The　new　method　has　many　applications　in　computer　vision　tasks.　The　paper　conducts　the　experiments　over　two　real　world　examples,　clustering　handwritten　digits　and　clustering　dynamic　textures.　The　experiments　show　the　proposed　method　outperforms　a　number　of　existing　methods.