Amyotrophic lateral sclerosis (ALS) is a fatal disorder with unknown etiology, in which genetic and environmental factors interplay to determine the onset and the course of the disease. Exposure to toxic metals has been proposed to be involved in the etiology of the disease either through a direct damage or by promoting oxidative stress. In this study we evaluated the concentration of a panel of metals in serum and whole blood of a small group of sporadic patients, all living in a defined geographical area, for which acid mine drainage has been reported. ALS prevalence in this area is higher than in the rest of Italy. Results were analyzed with software based on artificial neural networks. High concentrations of metals (in particular Se, Mn and Al) were associated with the disease group. Arsenic serum concentration resulted lower in ALS patients, but it positively correlated with disease duration. Comet assay was performed to evaluate endogenous DNA damage that resulted not different between patients and controls. Up to now only few studies considered geographically well-defined clusters of ALS patients. Common geographical origin among patients and controls gave us the chance to perform metallomic investigations under comparable conditions of environmental exposure. Elaboration of these data with software based on machine learning processes has the potential to be extremely useful to gain a comprehensive view of the complex interactions eventually leading to disease, even in a small number of subjects.