Abstract One of the most difficult tasks involved in the process of noise monitoring near airports is related to the automatic detection and classification of aircraft noise events. These tasks can be solved by applying pattern recognition techniques to the audio signal captured by a microphone. But now the problem is caused by the background noise, which is present in real environments. This paper proposes a real-time method for continuously tracking the similarity of the input sound and the aircraft’s sounds. Using these facilities, the monitoring unit will be able to mark aircraft events, or to make measurements only when aircraft sound is louder than background noise. A one-class approach has been applied to this detection-by-classification method. Using the default setup, 93% of the aircraft’s events which held an SNR of 6–8 dB were detected, for 30 different locations with diverse soundscapes.