Damage to wind turbine blades can, if left uncorrected, evolve into catastrophic failures resulting in high costs and significant losses for the operator. Detection of damage, especially in real time, has the potential to mitigate the losses associated with such catastrophic failure. To address this need various forms of online monitoring are being investigated, including acoustic emission detection. In this paper, pencil lead breaks are used as a standard reference source and tests are performed on unidirectional glass-fiber-reinforced-polymer plates. The mechanical pencil break is used to simulate an acoustic emission (AE) that generates elastic waves in the plate. Piezoelectric sensors and a data acquisition system are used to detect and record the signals. The expected dispersion curves generated for Lamb waves in plates are calculated, and the Gabor wavelet transform is used to provide dispersion curves based on experimental data. AE sources using an aluminum plate are used as a reference case for the experimental system and data processing validation. The analysis of the composite material provides information concerning the wave speed, modes, and attenuation of the waveform, which can be used to estimate maximum AE event – receiver separation, in a particular geometry and materials combination. The foundational data provided in this paper help to guide improvements in online structural health monitoring of wind turbine blades using acoustic emission.