Each year thousands of hectares of forest burnt across Southern Europe. To date, remote sensing assessments of this phenomenon have focused on the use of optical satellite imagery. However, the presence of clouds and smoke prevents the acquisition of this type of data in some areas. It is possible to overcome this problem by using synthetic aperture radar (SAR) data. Principal component analysis (PCA) was performed to quantify differences between pre- and post- fire images and to investigate the separability over a European Remote Sensing (ERS) SAR time series. Moreover, the transformation was carried out to determine the best conditions to acquire optimal SAR imagery according to meteorological parameters and the procedures to enhance burnt area discrimination for the identification of fire damage assessment. A comparative neural network classification was performed in order to map and to assess the burnts using a complete ERS time series or just an image before and an image after the fire according to the PCA. The results suggest that ERS is suitable to highlight areas of localized changes associated with forest fire damage in Mediterranean landcover.