The ability to use inexpensive, noninvasive sensors to accurately clas- sify flying insects would have significant implications for entomological re- search, and allow for the development of many useful applications in vector control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts on this task. To date, however, none of this research has had a lasting impact. In this work, we explain this lack of progress. We attribute the stagnation on this problem to several factors, including the use of acoustic sensing devices, the overreliance on the single feature of wingbeat frequency, and the attempts to learn complex models with relatively little data. In contrast, we show that pseudo-acoustic optical sensors can produce vastly superior data, that we can exploit additional features, both intrinsic and extrinsic to the insect’s flight behavior, and that a Bayesian classification ap- proach allows us to efficiently learn classification models that are very robust to overfitting. We demonstrate our findings with large scale experiments, as mea- sured both by the number of insects and the number of species considered.