This paper provides a new classification of Central–Southern Italian dialects using dialectometric methods. All varieties considered are analyzed and cast in a data set where homogeneous areas are evaluated according to a selected list of phonetic features. Using numerical evaluation of these features and the Manhattan distance, a linguistic distance rule is defined. On this basis, the classification problem is formulated as a clustering problem, and a k-means algorithm is used. Additionally, an ad-hoc rule is set to identify transitional areas, and silhouette analysis is used to select the most appropriate number of clusters. While meaningful results are obtained for each number of clusters, a nine-group classification emerges as the most appropriate. As the results suggest, this classification is less subjective, more precise, and more comprehensive than traditional ones based on selected isoglosses.
