Background: Sunflower (Helianthus annuus L.) is an important oilseed crop grown widely in various areas of the world. Classical genetic studies have been extensively undertaken for the improvement of this particular oilseed crop. Pertaining to this endeavor, we developed a "chemically induced mutated genetic resource for detecting SNP by TILLING" in sunflower to create new traits. Results: To optimize the EMS mutagenesis, we first conducted a "kill curve" analysis with a range of EMS dose from 0.5% to 3%. Based on the observed germination rate, a 50% survival rate i.e. LD50, treatment with 0.6% EMS for 8 hours was chosen to generate 5,000 M2 populations, out of which, 4,763 M3 plants with fertile seed set. Phenotypic characterization of the 5,000 M2 mutagenised lines were undertaken to assess the mutagenesis quality and to identify traits of interest. In the M2 population, about 1.1% of the plants showed phenotypic variations. The sunflower TILLING platform was setup using Endo-1-nuclease as mismatch detection system coupled with an eight fold DNA pooling strategy. As proof-of-concept, we screened the M2 population for induced mutations in two genes related to fatty acid biosynthesis, FatA an acyl-ACP thioesterase and SAD the stearoyl-ACP desaturase and identified a total of 26 mutations. Conclusion: Based on the TILLING of FatA and SAD genes, we calculated the overall mutation rate to one mutation every 480 kb, similar to other report for this crop so far. As sunflower is a plant model for seed oil biosynthesis, we anticipate that the developed genetic resource will be a useful tool to identify novel traits for sunflower crop improvement.