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 two 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.