In this paper we address the problem of reliably fitting parametric and semi-parametric models to high density spot array images obtained in gene expression experiments. The goal is to measure the amount of genetic material at specific spot locations. Many spots can be modelled accurately by a Gaussian shape. In order to deal with highly overlapping spots we use robust M-estimators. When the parametric method fails, we use a novel, robust semi-parametric method which can handle spots of different shapes accurately. We present the results for real data and compare the complexity of the two methods.