This study was carried out to classify 112 marine and estuarine sites of the southern Spanish coastline (about 918 km long) according to similar sediment characteristics by means of artificial neural networks (ANNs) such as Self-Organizing Maps (SOM) and sediment quality guidelines from a dataset consisted of 16 physical and chemical parameters including sediment granulometry, trace and major elements, total N and P and organic carbon content. The use of ANNs such as SOM made possible the classification of the sampling sites according to their similar chemical characteristics. Visual correlations between geochemical parameters were extracted due to the powerful visual characteristics (component planes) of the SOM revealing that ANNs are an excellent tool to be incorporated in sediment quality assessments. Besides, almost 20% of the sites were classified as medium-high or high priority sites in order to take future remediation actions due to their high mean Effects Range-Median Quotient (m-ERMQ) value. Priority sites included the estuaries of the major rivers (Tinto, Odiel, Palmones, etc.) and several locations along the eastern coastline.