Deep　neural　networks　(DNNs)　have　been　increasingly　adopted　in　various　applications.　Systematic　verification　and　validation　are　essential　techniques　to　guarantee　the　quality　of　such　systems.　Testing　is　one　of　the　feasible　solutions　for　system　validation.　However,　testing　DNNs　usually　requires　a　large　number　of　test　cases　and　leads　to　the　high　labeling　cost.　Meanwhile,　the　test　cases　may　contain　various　noisy　data,　which　also　increases　the　burden　of　testing.　In　this　paper,　we　put　forwards　a　test　case　prioritization　method　for　DNN-based　classifiers,　which　assigns　high　priorities　to　those　cases　that　can　lead　to　problematic　classifications.　The　test　cases　are　prioritized　w.r.t.　the　pattern　extracted　from　the　neuron　outputs.　The　difference　between　the　pattern　acquired　from　the　training　set　and　the　neurons　matched　from　a　given　input　determines　the　priority　of　the　input.　For　a　trained　model,　the　method　consists　of　two　steps.　First,　we　collect　neuron　output　values　over　the　training　set　and　construct　a　neuron-based　pattern　for　every　class　of　training　samples.　Second,　the　metrics　are　computed　according　to　the　comparison　between　neuron　output　values　of　the　DNN　model　from　an　input　and　the　selected　pattern.　We　carry　out　the　experimentation　over　three　popular　datasets　with　various　neural　network　structures.　The　extensive　experimental　results　demonstrate　that　the　efficiency　and　the　generalizability　of　the　prioritization　method　outperform　most　of　the　existing　techniques.　(C)　2021　Elsevier　B.V.　All　rights　reserved.