The inspection of production parts is a difficult, tedious and exhausting task, usually performed by human experts. Especially when it comes to small features, a manual inspectioncan be insufficient and susceptible to errors. To aid the quality control of transparent injection molded test stripe components and validate the color accuracy of installed reaction papers, a machine vision system is designed and build for the automatic inspection of the givenparts. This work applies a defect detection and classification approach which makes use ofthe difference in appearance of certain defect types depending on the way they are illuminated. In a following steps filters and other image processing methods are used to separatethe defect from the rest of the image. For each defect type, a specific lighting method andprocessing approach is chosen, which allows it to filter out all other defects. This approachis complemented by a convolutional neural network, trained for the classification of the teststripe components into ok and nok parts. For the task of color validation, a color calibrationalgorithm using a color chart is implemented and used as a basis for a referencing approach.In this approach the acquired color values of the sample are compared to a reference table,which allows it to translate them into pH or nitride values.The resulting system is capable of classifying the injection molded components with anaccuracy of over 98%, while classifying defects into dust, scratches, surface defects, distortions and inclusions. Working with the Lab color space, the implemented color validationachieves a root-mean square error of 6.35 for L, 2.77 for a and 4.36 for b, which translatesto an error of 0.2 when tested on a pH sample.