Incomplete data are common and require special techniques. The essential techniques are: marginalisation, imputation, and rough sets. The paper presents the imputation by inversion of the neuro-fuzzy system. First the neuro-fuzzy systems is trained with complete data. Next the system is inverted and the missing values are imputed. The complete and imputed data are used to train the final neuro-fuzzy system. The technique is limited to data items with one missing value. The paper is accompanied by numerical examples and statistical verification.