Joining processes play a critical role across several industries and manufacturing activities. One such activity is narrow gap TIG arc welding, present in a number of high value manufacturing domains the quality and outcomes are highly dependent on the operating parameters and the evolving state of the weld. Human supervision, and the application of vision systems to allow the operator to monitor the process as it is being undertaken is commonplace, however monitoring, characterisation and feedback in narrow gap use cases is less so. The work undertakes a comparative study into two different approaches to process monitoring of TIG arc welding. The first through more traditional machine vision (MV) methods, and the second through more modern deep neural network methods. Results show both methods developed are able to perform the role of process monitoring and defect detection with accuracy of 82% and 93% for the traditional and DNN methods respectively.