Experimental values for the liquid viscosity (η) at 20 °C ranging from 0.164 mPa·s (trans-2-pentene) to 1490 mPa·s (glycerol) have been collected from literature for 361 organic compounds containing C, H, N, O, S, and all halogens. Multiple linear regression (MLR) and two-layer neural network (NN) modeling (one hidden layer) with back-propagation have been applied to derive prediction methods for log η using nine descriptors as input. The analysis includes different partitionings of the data set into training and prediction sets and different numbers of hidden-layer neurons of the neural networks. For the linear and nonlinear models derived from a training set of 237 compounds, squared correlation coefficients of 0.92 and 0.93 as well as root-mean-square errors of 0.17 and 0.16 log units were achieved for a prediction set of 124 compounds, reflecting a reasonable accuracy for a wide range of chemical structures and viscosity values. However, only the NN model was capable of successfully treating glycerol with the maximum viscosity value, which was not possible with the MLR approach and with any other existing estimation scheme.