The classification methods Histogram of Oriented Gradients, Bag of Features, Support Vector Machines and Neural Networks are evaluated to find a fitting solution for the automatic classification of building plan components. These components feature shapes with little features and minor differences. After processing the building plans for the classification, feature analysis methods, as well as machine learning based approaches, are tested. The results of the classification methods are compared and the behaviors of the classification methods are analyzed. First results have shown, that neural network classification using line data extracted via Hough transformation and additional calculations surpass other classification methods tested in this work. It was found that the basic structure of building plan components can be detected with neural networks, but further improvements have to be made, if only a single classification process is to be relied on. In the future this work will be used to create 3D building models from 2D plans and enable agent based simulation in the models.