Consistent differences among melting curves of PCR-amplified DNA fragments are treated by normalizing the relative fluorescence units (RFU) and performing a clustering analysis, but statistically significant differences among curves are not usually determined. In the present study, an analysis based on functional data analysis (FDA) was implemented to evaluate the existence of statistically significant differences between normalized RFU curves obtained from PCR-HRM (high-resolution melting) analysis by using ANOVA for functional data. The effectiveness of the FDA method was analyzed with data from a set of samples of eight animal species of interest in food analysis, as well as mixtures of DNA from these species, analyzed by PCR-HRM to differentiate them. The statistical method described in this study has been demonstrated to be a robust and precise tool to discriminate among melting curves derived from HRM analysis. This method has advantages over the current comparison methods. PRACTICAL APPLICATION: As long as food fraud and mislabeling exist, new techniques for species identification are needed. High-resolution melting (HRM) has been shown to be a rapid, reliable and inexpensive species identification method. In the present study, functional data analysis (FDA) was applied to HRM curves of DNA from eight animal species used for food, as well as to mixtures of these species in different proportions. FDA has advantages over the usual methods, providing a deeper statistical analysis and facilitating the data interpretation as shown by the HRM analysis for a clearer comparison among individual species and mixtures of species.