he aim of the project is to verify potentia! artificial neural networks application as universal data analysis tools m epidemiology and pharmacoeconomics. Artificial neural networks are numbered among computational intelligence systems. Substantial difference between classical computational machines and those built on the neural paradigm is the latter ability to parallel processmg by set of independent units performing simple opera-tions (summation, function transformation). Redundancy and parallelism of computations are implicating -basic advantages of artificial neural net-works m comparison to classical computers like: robustness for the noise in the data, the ability for adaptation, self-organization. Primarily drug assessment was delimited only to its effectiveness. Nowadays pharmacoeconomics is a third part of drug therapy assessment after effectiveness and safety. Information used for exemplary prediction system construction comes from previous pharmacoeconomics analysis of Non-Small Cell Lung Cancer chemotherapy executed in Pharmacoepidemiology and Pharmacoeconomics Department. Depiction of using neural techniques in large, epidemiological datasets analysis is enabled by use of databases from National Center for Health Statistics (NCHS), Center for Disease Control and Prevention (CDC). Obtained results proofs that mentioned above assessmen ; ts.