Tahitian pearls are currently classified manually by pearl experts. This process is time consuming and furthermore subjective for some of the quality parameters such as colour, form or lustre. In this paper we present our ongoing work to implement an automatic classification of Tahitian pearls out of images. An automatic classification can help to accelerate the quality evaluation process as well as to minimize the subjective influence of the human classification. For this purpose different image segmentation and machine learning methods are used. In the following sections we explain our methodology, show first results for several sub-steps and give new ideas for greylevel edge detection and colour classification.