A　tunnel　boring　machine　(TBM)　generates　rock　chips　during　excavation,　which　are　crucial　for　assessing　surrounding　rock　integrity,　enhancing　excavation　efficiency,　and　evaluating　cutter　wear.　However,　traditional　methods　struggle　to　identify　small　rock　chips,　chips　submerged　in　soil　or　water,　and　chips　in　stacked　states.　This　paper　proposes　a　convolutional　neural　network　(CNN)-based　method　for　directly　recognizing　the　particle　size　distribution　from　rock　chip　images.　A　dataset　of　2520　rock　chip　images　representing　84　particle-size　distributions　was　collected　in　a　laboratory　environment.　By　comparing　various　CNN　architectures　and　hyperparameters,　an　optimal　model　was　obtained　with　a　mean　absolute　error　(MAE)　of　1.66　x　10-2　and　R2　of　0.923　on　the　test　set.　The　results　demonstrate　that　the　proposed　method　enables　the　real-time　recognition　of　particle　size　distribution　using　rock　chip　images,　which　has　the　potential　to　significantly　improve　intelligent　auxiliary　excavation　technology　in　TBMs.