This paper presents a novel artificial neural network (ANN)-based near-field microwave nondestructive testing technique for defect detection and classification in nonceramic insulators (NCI). In this paper, distribution class 33-kV NCI samples with no defects, air voids in silicone rubber and fiber glass core, cracks in the fiberglass core, and small metallic inclusion between the fiber core and shank were inspected. The microwave inspection system uses an open-ended rectangular waveguide sensor operating in the near-field at a frequency of 24 GHz. A data acquisition system was used to record the measured data. ANN was trained to classify the different types of defects. The results showed that all defects were detected and classified correctly with high recognition rates.