The uptake of a diverse set of 14C-labelled non-electrolytes by Brugia pahangi and Dipetalonema viteae was measured relative to the free diffusion of tritiated water. Inulin was used as a non-absorbable surface marker to account for non-electrolyte adherent to the surface of the parasite which had not crossed the cuticle. B. pahangi and D. viteae took up the non-electrolytes to a similar degree; a comparison of tissue uptake indices gave a correlation coefficient of 0.99. Worm uptake could not be described by non-electrolyte octanol/aqueous partition coefficients alone. However, greater success was achieved using further descriptors and pattern recognition techniques for data analysis. The whole molecule descriptors log P, molar refraction, melting point, dipole moment and CNDO total energy were obtained from computer chemistry and the literature. Using a linear learning machine to relate uptake to these 5 physicochemical descriptors it was possible to successfully classify non-electrolytes as high or low uptake. Multivariate regression analysis of uptake versus these 5 parameters gave a correlation coefficient of 0.77. However, this was not statistically significant and therefore could not be used for quantitative predictions of substance uptake by worms. This illustrates the value of 'pattern recognition' techniques such as the linear learning machine. Using such 'pattern recognition' methods on a chemically related set of compounds it is anticipated that predictions of uptake can be achieved and improved upon. Such predictions could then be used in drug design.