In this paper a new approach for classification is presented, the "Fast Projection Plane Classifier", abbreviated here to FPPC. The main idea of FPPC is very simple: A classification problem for n-dimensional feature vectors is transformed into several 2 dimensional (2d) classification problems. Each transformation is a projection of the feature space into a plane. The projection into 2d-planes simplifies the classification task massively and leads to a very fast classification algorithm which uses binary lookup tables as a representation of the distribution of the previously trained feature vectors. The algorithm has some beneficial properties: it can handle high dimensional problems without an explosion of the number of required training samples, and with all possible 2d-projections the algorithm uses a reliable and precise representation of the training samples.