Online monitoring of friction stir welding (FSW) is inevitable due to the increasing demand of this process. Also the machine vision system has industrial importance for monitoring of manufacturing processes due to its non-invasiveness and flexibility. Therefore, in this research, an attempt has been made to monitor friction stir welding process by analyzing the weld surface images. Here, discrete wavelet transform has been applied on FSW images to extract useful features for describing the good and defective weld. These obtained features have been fed to support vector machine based classification model for classifying good and defective weld with 99\% and 97\% accuracy with Gaussian and polynomial kernel, respectively. © 2015 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.