1. A method for determining a type of a defect in a weld comprising: determining a defect location and a defect signal corresponding to the defect location by analyzing ultrasonic response signals collected from a plurality of measurement locations along the weld; inputting the defect signal and a plurality of defect proximity signals into a trained artificial neural network, wherein the plurality of defect proximity signals correspond to ultrasonic response signals from measurement locations on each side of the defect location and the trained artificial neural network is operable to: identify the type of the defect located at the defect location based on the defect signal and the plurality of defect proximity signals; and output the type of the defect located at the defect location.