An optical sensor was used to make digital recordings of wingbeat waveforms for the five most common aphids found on Guam: Aphis craccivora Koch, A. gossypii Glover, A. nerii Fonscolombe, Pentalonia nigronervosa Coquerel, and Toxoptera citricida (Kirkaldy). Wingbeat frequencies for each species overlapped all other species. However, mean wingbeat frequencies were significantly different for all species. Wingbeat frequencies and harmonic patterns were extracted from the recordings and submitted to cluster analysis, which failed to separate species completely. Several nearest neighbor and probabilistic neural network classifiers were built using time series, frequency spectra, wingbeat frequencies, and harmonic patterns as input variables. These classifiers were evaluated by having them identify wingbeat waveforms from aphids collected and recorded after their construction. The best performing classifier model was a probabilistic artificial neural network trained using 256-bin frequency spectra as input. Sixty-nine percent of the waveforms presented to this network were identified correctly. This study demonstrates the feasibility of developing an insect flight monitor that automatically counts and identifies individual flying insects. Essential components of the monitoring system are a photosensor, a multimedia personal computer, and software that identifies wingbeat frequency spectra using an artificial neural network.