Oil palm is Malaysia's major agriculture product and it covers approximately 5 million hectares of Malaysia's land. Limited land resources have been an important factor that motivated the need to increase oil extraction rate (OER). OER of oil palm fresh fruit bunches (FFB) depends highly on their maturity stage. The ripe oil palm FFB will produce high OER while the under ripe and over ripe oil palm FFB will produce less oil. Thus, this paper presents a method of classification between oil palm FFB into ripe, under-ripe and over-ripe categories. This research was done at an oil palm plantation in peninsular Malaysia. A total of two-hundred and ten oil palm FFB that consist of seventy bunches for each category of under-ripe, ripe and over-ripe had been used. Each bunch was scanned ten times randomly with a hand-held multi-parameter fluorescence sensor called Multiplex(R)3. The parameter measured was the Blue-to-Red Fluorescence Ratio (BRR_FRF) obtained from blue-green (447 nm) and far-red (685 nm) emission signal by using ultraviolet (UV) light emitting diode as excitation light source. The novel contribution of this research is to prove that the oil palm FFB maturity can be determined using the Blue-to-Red Fluorescence ratio index. This is based to our finding of a significant difference among the three categories of ripeness based on the parameter. Classification and Regression Tree (C&RT) method was proposed in this paper. Hundred-fifty samples were used to develop the model by trained it using C&RT method and the remaining sixty samples for the test component. By using the C&RT method, the results show the best accuracy of overall testing classification is 90%. This research will be useful for future development of non-destructive, automatic and real time oil palm FFB grading system. (C) 2012 Published by Elsevier Ltd.