Artificial intelligence and radiomics have the potential to revolutionise cancer prognostication and personalised treatment. Manual outlining of the tumour volume for extraction of radiomics features (RF) is a subjective process. This study investigates robustness of RF to inter-observer variation (IOV) in contouring in lung cancer. We utilised two public imaging datasets: ���NSCLC-Radiomics��� and ���NSCLC-Radiomics-Interobserver1��� (���Interobserver���). For ���NSCLC-Radiomics���, we created an additional set of manual contours for 92 patients, and for ���Interobserver���, there were five manual and five semi-automated contours available for 20 patients. Dice coefficients (DC) were calculated for contours. 1113 RF were extracted including shape, first order and texture features. Intraclass correlation coefficient (ICC) was computed to assess robustness of RF to IOV. Cox regression analysis for overall survival (OS) was performed with a previously published radiomics signature. The median DC ranged from 0.81 (���NSCLC-Radiomics���) to 0.85 (���Interobserver������semi-automated). The median ICC for the ���NSCLC-Radiomics���, ���Interobserver��� (manual) and ���Interobserver��� (semi-automated) were 0.90, 0.88 and 0.93 respectively. The ICC varied by feature type and was lower for first order and gray level co-occurrence matrix (GLCM) features. Shape features had a lower median ICC in the ���NSCLC-Radiomics��� dataset compared to the ���Interobserver��� dataset. Survival analysis showed similar separation of curves for three of four RF apart from ���original_shape_Compactness2���, a feature with low ICC (0.61). The majority of RF are robust to IOV, with first order, GLCM and shape features being the least robust. Semi-automated contouring improves feature stability. Decreased robustness of a feature is significant as it may impact upon the features��� prognostic capability.