Rapid and relatively accurate knowledge about the severity and the extent of building damage is essential in managing crucial activities shortly after disastrous earthquakes. In recent years, Very High Resolution (VHR) optical satellite imagery systems have provided important sources for such information. In this research, a method of automated building extraction and damage detection using image processing techniques are presented. The case study was chosen as the 2003 Bam earthquake where VHR QuickBird images of the before and after event were acquired. After coregistration and data fusion steps, an object-oriented clustering methodology was performed using scale, shape and compactness parameters and different urban features were categorized according to a supervised classification scheme. Building extraction results where compared with an existing urban database showing an overall accuracy of 91%. Damage mapping was completed based on first-order and Haralick second-order textural features for three damage grades as slight, extensive and destruction using pre and post event images. The damage was classified according to an Artificial Neural Network (ANN) using contrast, second moment, mean and antropy as an optimal feature set. The overall accuracy for damage mapping using second order features is reported as 73%.