Identifying individual instruments of the same type from their recordings is a rarely addressed challenge. This paper explores violin sound identification using two datasets of recordings, featuring multiple violinists playing multiple violins. Several long-term audio features were compared, and their performance in violin classification was evaluated using classical machine learning algorithms. Among these features, long-term MFCCs demonstrated the ability to distinguish individual violins, even with player-induced variability, enabling reliable violin recognition. Additionally, the influence of key parameters—such as the number of violinists, recording duration, and choice of musical excerpts—on classification performance was analyzed. These findings offer guidelines for optimizing future data collection aimed at capturing a violin’s unique sound signature.