While digital displays and glass cockpits have become widespread in modern aircraft, analog instruments remain. These gauges can be challenging to digitize or integrate into automated safety systems. This work investigates the application of computer vision to evaluate aircraft instruments of varying complexity. For ease of acquisition, training data was recorded from a flight simulator and used to train neural networks. The resulting models have high accuracy when evaluating single pointer gauges in lighting conditions similar to the training data set, as well as with entirely different lighting conditions. Performance remains robust even with more complex instruments, such as dual pointer airspeed gauges and attitude indicators. Potential future work on this system includes applying it to real-life aircraft and integration with safety systems, including detection of instrument display failures.