We investigate the use of perceptrons for classification of microarray data. Small round blue cell tumours of childhood are difficult to classify both clinically and via routine histology. Khan et al. [10] showed that a system of artificial neural networks can utilize gene expression measurements from microarrays and classify these tumours into four different categories. We used a simulated annealing-based method in learning a system of perceptrons, each obtained by resampling of the training set. Our results are comparable to those of Khan et al., indicating that there is a role for perceptrons in the classification of tumours based on gene expression data. We also show that it is critical to perform feature selection in this type of models.