Radio frequency fingerprinting has been identified as a method to increase integrity in aircraft surveillance while retaining its openness. One way to uniquely determine transmitting devices is to distill the device its radio frequency (RF) fingerprint by looking at the physical features of the message signal it transmits. This physical layer fingerprint is the unique trace the transmitter leaves in the signals. This research proposes a method to RF fingerprint ADS-B and VDL2 messages to identify the transmitting aircraft using a complex-valued convolutional neural network model. Raw data from ADS-B and VDL2 messages are collected over multiple days using low-cost RTLSDR hardware. Results show that the model can identify ADS-B and VDL2 messages from up to 200 different aircraft based on the raw IQ preamble and bit synchronization samples of both signal protocols. Further analysis of the robustness of the model shows that the model accuracy can be highly affected by changing channel conditions during training and testing. This research shows that testing the RF fingerprinting model’s robustness to channel conditions is necessary since the models are prone to mistakenly considering channel information as transponder RF features.