This text presents a novel architecture for binary classification. The idea is the combination of the Gauss-Helmert model with Fisher's linear discriminant/least squares techniques. The result of this combination is a linear classifier that is able to use prior knowledge in the form of information about the uncertainty of the input data points for the learning of the decision boundary. This uncertainty information is usually given in the form of a covariance matrix and it is possible to use different covariance matrices for different points. The use of error propagation enables this system not only to use uncertainty information for the learning, it also results in uncertainty information for the decision boundary. The uncertainty information of the decision boundary can be used to calculate a measure of confidence for the classification result for a new point. Alternatively it can be used by a statistical test to determine whether a new point can be classified with sufficient reliability or not. Besides the linear classifier, an extension to non-linear classification is presented too. This extension is based on the ideas of radial basis function (RBF) networks. Some initial experiments giving a proof of concept are reported as well.