The aim of the project was to generate cost estimation formulae using the minimum amount of design information. Capital cost data for pressure vessels were analysed with a range of techniques including regression analysis, neural networks, rational polynomials, non-linear equations and fuzzy matching. Two sets of real industrial cost information were studied. The results showed that regression analysis using a power-law (or multiplicative function) gave convenient and reasonable cost correlations. The best overall technique was a neural network having a particularly simple structure (i.e., a single hidden layer with just one or two elements within it).