The independent component analysis is a powerful technique in extraction of features (with non-gaussianity distribution) in data through blind source separation from noises. Selection of independent components plays the main role in interpretation of results. To prepare for non-Gaussian mode, the data must be converted to the standard form (unit variance and zero mean model). In the majority of algorithms, functions are defined in this method that processes data as non-Gaussian (Super Gaussian -under Gaussian). In the following study 137 soil samples (collected from 0 to 30 cm depth) digested by four-acid method (HCl–HNO3–HClO4–HF) and subsequently analyzed for elements-45 y ICP-OES were used. Data were subjected to independent component analysis and eventually 7 independent components (IC) were identified and the detected loadings of elements in each IC were extracted. By comparing the results with PCA and HCA (hierarchICal clustering) methods, the elements of As, Ba, Mn, P, Cu, Pb, Cd and Zn can be considered as factors affecting environmental contaminations. In order to evaluate the metal contaminations condition, mapping the environmental pollution indICes in contaminated areas with Arc-GIS software was performed and the values found were compared with the standard of quality of Iranian soil resources