Distinctive individual vocalizations are advantageous in several social contexts. Both genetic and environmental effects are responsible for this phenomenon resulting in different frequencies and time domains of sounds in birds. This individuality can be utilized in breeding bird censuses and abundance estimates. In this study we explored the individuality of the advertisement calls of male Common Cuckoos (Cuculus canorus) with the aims of describing the ways in which individuals differ from each other acoustically, and characterizing the practical requirements for using statistical learning methods for individual recognition. We collected calls from a Hungarian Cuckoo population and conducted discriminant function analysis on acoustic parameters to distinguish individuals. We show that individuals differ in both the frequency and time of their calls, most importantly in maximum frequency of the first syllable. Our discrimination of the calls of 26 male individuals was almost 100 % accurate, and even when the number of samples was reduced to five calls per individual, and the number of acoustic parameters was decreased to five variables, accuracy still exceeded 90 %. Our acoustic individual discrimination technique is applicable to a wide range of ecological and behavioural studies because it is easy to perform and can be readily automated.