Access to the complete gene inventory of an organism is crucial to understanding physiological processes like development, differentiation, pathogenesis, or adaptation to the environment. Transcripts from many active genes are present at low copy numbers. Therefore, procedures that rely on random EST sequencing or on normalisation and subtraction methods have to produce massively redundant data to get access to low-abundance genes. Here, we present an improved oligonucleotide fingerprinting (ofp) approach to the genome of sugar beet (Beta vulgaris), a plant for which practically no molecular information has been available. To identify distinct genes and to provide a representative 'unigene' cDNA set for sugar beet, 159 936 cDNA clones were processed utilizing large-scale, high-throughput data generation and analysis methods. Data analysis yielded 30 444 ofp clusters reflecting the number of different genes in the original cDNA sample. A sample of 10 961 cDNA clones, each representing a different cluster, were selected for sequencing. Standard sequence analysis confirmed that 89% of these EST sequences did represent different genes. These results indicate that the full set of 30 444 ofp clusters represent up to 25 000 genes. We conclude that the ofp analysis pipeline is an accurate and effective way to construct large representative 'unigene' sets for any plant of interest with no requirement for prior molecular sequence data.