The inspection of production parts is a difficult, tedious and exhausting task, usually per-formed by human experts. Especially when it comes to small features, a manual inspection can be insufficient and susceptible to errors. To aid the quality control of transparent injec-tion molded test stripe components and validate the color accuracy of installed reaction pa-pers, a machine vision system is designed and build for the automatic inspection of the given parts. This work applies a defect detection and classification approach which makes use of the difference in appearance of certain defect types depending on the way they are illumi-nated. In a following steps filters and other image processing methods are used to separate the defect from the rest of the image. For each defect type, a specific lighting method and processing approach is chosen, which allows it to filter out all other defects. This approach is complemented by a convolutional neural network, trained for the classification of the test stripe components into ok and nok parts. For the task of color validation, a color calibration algorithm 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 an accuracy of over 98%, while classifying defects into dust, scratches, surface defects, distor-tions and inclusions. Working with the Lab color space, the implemented color validation achieves a root-mean square error of 6.35 for L, 2.77 for a and 4.36 for b, which translates to an error of 0.2 when tested on a pH sample.