The use of wastepaper as a raw materials within the paper making industry is steadily increasing. One of the key elements in processing wastepaper is the removal of ink. Current research in the industry is focused on the capability of measuring particle size on-line, within a paper mill environment. This paper describes the development of a prototype machine vision system for the analysis of ink particles within wastepaper pulp samples. A priori knowledge of the domain has been exploited to produce a lighting and sample presentation system which maximizes ink particle contrast within the image. The system has been used to examine samples from a pilot deinking cell at various stages of operation. This gives a wide spectrum of sample compositions typically encountered within a mill environment. One of the key areas in a fully automated analysis system is the segmentation of the images to determine ink particle content. Simple thresholding techniques provide the fast solution required to analyze the high throughput of a deinking line. Due to the large variation in image content the development of an overall segmentation strategy incorporating several algorithms is proposed. A suitable segmentation process is selected according to a priori knowledge of the sample content and speed of operation required. The performance limitations for particular algorithms have been determined to aid the selection process.