Abstract
Thermoplastics injection molding is a manufacturing process used for mass-production of plastic parts. The process includes four main stages during which material used goes through complicated thermo-mechanical changes. In order to make the process more controllable and repeatable it is, at first, necessary to understand which parameters are the most important ones. The following paper describes how application of statistical feature selection methods, such as Information gain and ReliefF, allows to identify which injection molding parameters have a greater influence on the final part quality. The article gives short description of the above-mentioned methods and shows what were results of their application on dataset obtained from 160 machine runs, during which 41 machine and process parameters were logged.