Advances powered by artificial intelligence (AI)-centric technologies have enveloped nearly every aspect of our lives. Seven patterns of AI have been classified, the most common being the recognition pattern. This chapter focuses on pattern recognition with a subset emphasis on image and sound as it may relate to NDE 4.0. Optical character recognition (OCR) has utilized image recognition since the 1920s, from character-to-telegraph conversion to document processing, passport verification, and check deposits. These applications establish a precedent for AI-driven flaw detection in radiographic imaging. Computer vision (CV) is the base building block for extraction of data from an image and can recognize objects using algorithms and machine learning concepts. Computer vision has been integral in detection, segmentation, classification, monitoring, and prediction of radiographs in the medical community and has applicability in visual and radiographic inspection in industry and the NDE community. Sound recognition has a large portion of defect formations and flawed mechanical movements release energy in the method of elastic waves with the broad frequency spectrum. Typically, these signals are digitized and converted into amplitude time series. Regardless of their frequency content, these digital acoustics or sound signals can be analyzed and classified by any method that applies to time series data, including those developed specifically for audible sound signals such as deep learning algorithms.