The Rufous Hornero (Furnarius rufus), a highly abundant suboscine and Argentina’s national bird, has a song long considered innate, but it displays a higher degree of complexity than typically observed in this clade. Here, we examined the acoustic properties of Hornero songs to test whether syllables carry individual signatures. Using quantitative acoustic analyses and machine learning, we trained Siamese Neural Networks on spectrotemporal representations of the fundamental frequency to classify individuals. Our results show that female horneros produce identifiable acoustic signatures, whereas male songs lack sufficient distinctiveness for reliable classification. Detailed spectrographic analyses of female syllables, combined with biomechanical modeling of the sound source, suggest fine motor control as the basis of these individual acoustic fingerprints. These findings highlight the importance of studying vocal production and neuromuscular control in suboscines, particularly the role of individuality in song.