Deep　neural　networks　(DNNs)　have　been　increasingly　adopted　in　various　applications.　Systematic　verification　and　validation　is　essential　to　guarantee　the　quality　of　such　systems.　Due　to　the　scalability　problem,　formal　methods　can　hardly　be　widely　applied　in　practice.　Testing　is　one　of　feasible　solutions.　However,　lacking　of　input　space　specification　for　DNNs　requires　a　large　set　of　test　cases　to　he　constructed　to　increase　the　testing　adequacy,　which　leads　to　high　labeling　cost　of　test　cases.　In　this　paper,　we　put　forwards　a　test　case　prioritization　method　14　DNN　classifiers,　which　assigns　high　priorities　to　those　cases　that　could　lead　to　wrong　classifications.　The　priorities　are　calculated　according　to　the　activation　pattern　of　neurons　acquired　from　the　training　sets　and　the　activated　neurons　collected　from　certain　inputs.　For　a　trained　model,　the　method　consists　of　two　steps.　First,　we　accumulate　neuron　activation　patterns　over　the　training　set,　and　construct　a　set　of　frequently　activated　neurons　based　on　the　frequency　(times)　of　activation　for　every　class.　Second,　the　metrics　are　computed　according　to　the　comparison　between　the　activated　neurons　of　an　input　and　the　selected　set　of　frequently　activated　neurons　with　iLs　output　The　experimentation　is　carried　out　over　three　popular　datasets　with　various　neural　network　structures.　The　results　demonstrate　that　the　test　cases　with　higher　priorities　are　more　prone　to　be　mis-classified.　And　the　prioritized　lest　cases　over　a　DNN　model　within　same　datasets　are　also　efficient　in　triggering　mis-classilication　of　other　DNNs　with　similar　structures.