Recurring expenses associated with preventative maintenance and inspectionproduce operational inefficiencies and unnecessary spending. Human inspec-tors may submit inaccurate damage assessments and physically inaccessiblelocations, like underground mining structures, and pose additional logisticalchallenges. Automated systems and computer vision can significantly reducethese challenges and streamline preventative maintenance and inspection.The authors propose a convolutional neural network (CNN)‐based approachto identify the presence and type of structural damage. CNN is a deep feed‐for-ward artificial neural network that utilizes learnable convolutional filters toidentify distinguishing patterns present in images. CNN is invariant to imagescale, location, and noise, which makes it robust to classify damage of differentsizes or shapes. The proposed approach is validated with synthetic data of acomposite sandwich panel with debonding damage, and crack damage recogni-tion is demonstrated on real concrete bridge crack images. CNN outperformsseveral other machine learning algorithms in completing the same task. Theauthors conclude that CNN is an effective tool for the detection and typeidentification of damage.