The growing use of complex and irregularly shaped components for safety-critical applications has increasingly led to the adoption of X-ray CT as an NDE inspection tool. Standard X-ray CT methods require thousands of projections, each regularly distributed evenly through 360∘ to produce an accurate image. The time consuming acquisition of thousands of projections can lead to significant bottlenecks. Recent developments in medical imaging driven by both increasing computational power and the desire to reduce patient X-ray exposure have led to the development of a number of limited view CT methodologies. Thus far these limited view algorithms have been applied to basic synthetic data derived from simple medical phantoms. Here, we use experimental data to rigorously test the capability of limited view algorithms to accurately reconstruct and precisely measure the dimensional features of an additive manufactured sample and a turbine blade. Our findings highlight the importance of prior information in producing accurate reconstructions capable of significantly reducing X-ray projections by at least an order of magnitude. In the turbine blade example a dramatic reduction in projections from 5000 to 24 was observed while still demonstrating the same level of accuracy as standard CT methods. The findings of the study also suggest the importance of sample complexity and the presence of sparsity in the X-ray projections in order to maximise the capabilities of these limited algorithms. With the ever increasing computational power limited view CT algorithms offer a method for reducing data acquisition time and alleviating manufacturing throughput bottlenecks without compromising image accuracy and quality.