In order to develop an effective and accurate way to monitor and control the quality of fiberglass products, Acoustic Emission (AE) signals, generated during compression of fiberglass samples, were studied and analyzed using neural network based pattern recognition software. Distinguishable patterns were found in samples manufactured under different conditions and compositions, which resulted in different product quality. AE waveform features, such as absolute energy, average frequency, duration, and rise time were analyzed and the features showed strong dependence on the sample tested. This made sample classification possible and definitive and therefore a classifier was developed and applied to data collected from additional test samples. Finally, an AE system for the evaluation of fiberglass insulation was designed and built. It is expected that the developed system will be used as a quality control tool in industrial production of fiberglass insulating material. In this paper we will discuss the AE data collection and analysis, classifier development, and give an overview of the inspection system developed.