The field of computer vision offers tools that allow machines to derive meaningful infor-mation from video and images and consequently make decisions based on visual inputs. In the paper industry, implementation of machine vision (MV) can be used to automate and speed up processes that require visual inspection, particularly certain segments of quality control – one such application being detection and measurement of paper roll core eccentricity. Core eccentricity is a roll build error in which the roll core is offset from the geometric roll center, potentially causing runnability issues. This particular project aims to improve the detection of paper roll core eccentricity at the Mondi Dynäs integrated pulp and paper mill through creation, calibration and evaluation of a machine-vision-based tool for on-line core eccentricity measurement. The tool utilizes the Hough Transform (HT), since HT is a simple yet fast and robust algorithm when it comes to identification of basic shapes such as lines and circles. The proposed solution was evaluated in two ways; firstly by determining at what level of accuracy the measurements could be provided, accounting for how well the solution deals with correction of systematic error caused by environmental factors, and secondly by analyzing how well characteristic roll features could be accurately identified in large sets of data, necessary to consistently perform measurements. The evaluation of the proposed solution showed a 99.9% detection rate for characteristic paper roll features, and a 98.1% detection rate of laser lines used for correction of position and orientation induced error. Assessment of the measurement accuracy following successful detection was on par with the current optical measurement method, and the proposed solution was able to classify distinctive features with a 96.8% accuracy. Lastly, several improvement actions to address faulty detection were identified, and factors to be considered for future installment were highlighted.