Oil spill detection methodologies traditionally use arbitrary selected quantitative and qualitative statistical features (e.g. area, perimeter, complexity) for classifying dark objects on SAR images to oil spills or look-alike phenomena. In our previous work genetic algorithms in synergy with neural networks were used to suggest the best feature combination maximizing the discrimination of oil spills and look-alike phenomena. In the present work, a detailed examination of robustness of the proposed combination of features is given. The method is unique, as it searches though a large number of combinations derived from the initial 25 features. The results show that a combination of 10 features yields the most accurate results. Based on a dataset consisting of 69 oil spills and 90 look-alikes, classification accuracies of 85.3% for oil spills and in 84.4% for look-alikes are achieved.