This work is devoted to establishing a quantitative structure-property relationship (QSPR) between the chemical structure of ionic liquids (ILs) and their viscosity followed by computer-aided design of new ILs possessing desirable viscosity. The modeling was performed using back-propagation artificial neural networks on a set of 99 ILs at 25 degrees C, covering a large viscosity range from 3 to 800 cP. The ISIDA fragment descriptors were used to encode molecular structures of Its. These models were first validated on 23 new Its from Solvionic company and then used to predict the viscosity of three new ILs which then have been synthesized and tested. The models display high predictive performance in external 5-fold cross validation: determination coefficients R-2 > 0.73 and absolute mean root mean square error < 70 cP. For three ILs synthesized and tested in this work, predicted viscosities are in good qualitative agreement with the experimentally measured ones.