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This paper presents an experimental study report of motion blur in a coordinate measuring machine (CMM) for dynamic measurements of geometrical features and validates a mathematic model. This model describes the relation between the CMM information about velocity, exposure time of camera, magnification of the lens, distance of adjacent pixels etc. and the grade of motion blur in the image. Three of these CMM input parameters are adjustable and were tested in experiments recording images for the analysis of motion blur. Also a new and robust method was used to estimate the grade of motion blur in the images statically. This new method is described in [2] in detail. It cooperates a fitting of an edge describing function and the knowledge of the photometrical effects at the intensity edge transition to calculate the blurred edge width (BEW) accurately and robustly. It has advantage to handle the significant blurred edge transition even with great superposed noise. The results of the experiments are figured in the diagrams. It has proved the validity of the proposed model, in short, the CMM parameters are proportional to the grade of the motion blur in the images. The work to validate this model is at first to determine the optimal operating point of CMM for dynamic measurement. Secondly it is very easy to determine the grade of motion blur directly from the model and further to generate a point spread function (PSF), which is used further for deconvolution. If there is blur as a fact of diffraction or defocus except for motion blur, the PSF of these blurs can be estimated by subtraction of the model result and the one of the new BEW calculation method in the form of a Gauss PSF. Thirdly with the CMM velocity information in the x- and y-axis, it can be easily and accurately determined the angle of the motion blur then it is easy to set a search line in in CMM to calculate BEW and it is possible to generate a 2D PSF.