Acquisition of microarray data is prone to systematic errors. A correction, called normalisation, must be applied to the data before further analysis is performed. With many normalisation techniques published and in use, the best way of executing this correction remains an open question. In this study, a variety of single-slide normalisation techniques, and different parameter settings for these techniques, were compared over many replicated microarray experiments. Different normalisation techniques were assessed through the distribution of the standard deviation of replicates from one biological sample across different slides. It is shown that local normalisation outperformed global normalisation, and intensity-based 'LOWESS' outperformed trimmed mean and median normalisation techniques. Overall, the top performing normalisation technique was a print-tip-based LOWESS with zero robust iterations. Lastly, we validated this evaluation methodology by examining the ability to predict oestrogen receptor-positive and -negative breast cancer samples with data that had been normalised using different techniques.