Sparse　representation　is　a　powerful　tool　in　many　visual　applications　since　images　can　be　represented　effectively　and　efficiently　with　a　dictionary.　Conventional　dictionary　learning　methods　usually　treat　each　training　sample　equally,　which　would　lead　to　the　degradation　of　recognition　performance　when　the　samples　from　same　category　distribute　dispersedly.　This　is　because　the　dictionary　focuses　more　on　easy　samples　(known　as　highly　clustered　samples),　and　those　hard　samples　(known　as　widely　distributed　samples)　are　easily　ignored.　As　a　result,　the　test　samples　which　exhibit　high　dissimilarities　to　most　of　intra-category　samples　tend　to　be　misclassified.　To　circumvent　this　issue,　this　paper　proposes　a　simple　and　effective　hardness-aware　dictionary　learning　(HADL)　method,　which　considers　training　samples　discriminatively　based　on　the　AdaBoost　mechanism.　Different　from　learning　one　optimal　dictionary,　HADL　learns　a　set　of　dictionaries　and　corresponding　sub-classifiers　jointly　in　an　iterative　fashion.　In　each　iteration,　HADL　learns　a　dictionary　and　a　sub-classifier,　and　updates　the　weights　based　on　the　classification　errors　given　by　current　sub-classifier.　Those　correctly　classified　samples　are　assigned　with　small　weights　while　those　incorrectly　classified　samples　are　assigned　with　large　weights.　Through　the　iterated　learning　procedure,　the　hard　samples　are　associated　with　different　dictionaries.　Finally,　HADL　combines　the　learned　sub-classifiers　linearly　to　form　a　strong　classifier,　which　improves　the　overall　recognition　accuracy　effectively.　Experiments　on　well-known　benchmarks　show　that　HADL　achieves　promising　classification　results.