The influence of ripening degree of drupes during the harvesting period is well established in olive oil sector. A range of methods for expressing the stage of maturity of olives have been proposed in scientific literature. One of the most commonly adopted methods provides the evaluation of a Ripening Index (RI) on the basis of olive skin and pulp colour. Unfortunately, the RI evaluation technique is time-consuming, subjective (depending on expert skill) and depends on environmental conditions that may affect colour appearance of olives. This work describes a novel method for rapid, automatic and objective prediction of the Ripening Index of an olive lot. The method integrates a Machine Vision system, capable of performing a colour-based raw prediction of RI, with an Artificial Neural Network (ANN) based algorithm to refine it. Such a refinement is based on a set of chemical parameters (oil content, sugar content and phenol content) which are provided as input to the ANN and which can be obtained by historical curves for the region where the RI needs to be predicted. Experimental results demonstrate the effectiveness of the proposed approach. (c) 2010 Elsevier Ltd. All rights reserved.