Traditional plant design procedures seldom produce the optimum design for given plant parameters. Non-optimum design is due to a variety of factors including, amongst others, a poorly structured approach, reluctance to undertake rime consuming iterative design, lack of equipment knowledge and the bias of design due to personal preferences. This paper presents derails of a new technique for mineral processing plant synthesis. Using an approach incorporating various aspects of Artificial Intelligence, including Learning Classifier Systems, the idea is to create self-contained plant units that possess knowledge of applicability from within. The process objects then bid for an appropriate position in the plant. As a processing plant is a multi-component process the Intelligent Process Plant Object (IPPO) is used to advise on possible plant interactions during the bidding process. Using this approach it is believed that the proposed technique will use up-to-date knowledge and resources in a more efficient manner than conventional methods. As part of the development of the synthesis technique the paper describes a case study comprising of a three stage crushing and screening circuit used in the quarrying industry. The new synthesis technique is applied to the manual plant design in order to determine if the approach is capable of replicating the original circuit. The result of this analysis is presented. The idea of intelligent objects representing the plant and the subsequent synthesis of a circuit via bidding and competition has great potential for the minerals＇ industry. It is hoped that this procedure will save the design engineer considerable time, reducing design expenses, and thus allowing the user to get closer to the goal of optimum plant design for given input and product requirements.