Quantitative structure-activity relationship study was performed to understand analgesic activity for a set of 95 heterogeneous analgesic compounds. This study was performed by using the principal component-artificial neural network modeling method, with application of eigenvalue ranking factor selection procedure. The results obtained by principal component-artificial neural network give advanced regression models with good prediction ability using a relatively low number of principal components. A 0.834 correlation coefficient was obtained using principal component-artificial neural network with six extracted principal components.