This paper presents an approach to defect detection and characterisation in ultrasonic inspection of laminate composite panels. A set of features coupled with gates was identified along with a method for sub-dividing and thresholding the ultrasonic data, which removes most of the location specific information from the defect data thus increasing the generalisation capabilities of the defect classifier. Validation results obtained from independent defect data indicate that the performance of the presented data description tools coupled with an artificial neural network classifier was able to correctly locate and classify defects at different depths. It was concluded that a structured approach to the pre-processing of ultrasonic testing data, combined with the selective feature extraction for artificial neural network classification, greatly reduces the requirement for artificial neural network training data. Furthermore, it allows for improved performance across a variety of panel geometries.