We propose a shape analysis system based upon the description of landmarks with measurement covariance, which extends statistical linear modelling processes to ‘pseudo landmarks’ for scientific studies. We discuss the properties of our approach and how measurement covariances can be considered characteristic of the local shape. Our formulation includes corrections for parameter bias, induced by the degrees of freedom within the linear model. The method has been implemented and tested on measurements from fly wing, hand and face data. We use these data to explore possible advantages and disadvantages over the use of standard Procrustes/PCA analysis. In the process we show how appropriate weighting provides more efficient use of the available landmark data 1 .