Abstract Fuel viscosity is an important property that has a significant effect in fuel injection, spray development and combustion in Compression Ignition (CI) engines. Current and future injector designs of diesel engines (such as rail injection systems) work at high pressures (>100 MPa), meaning that fuel viscosity increases substantially over the atmospheric values. The estimation of biodiesel (BD) viscosity based on the knowledge of its composition would be of great potential in the optimization of biodiesel production processes, particularly in the blending of raw materials and refined products. In this work, comprehensive data sets were chosen from literature regarding several BD classes, in order to establish new correlations and new predictive methods of viscosity. The proposed methodologies were validated using available viscosity data of BDs having different chemical compositions in wide ranges of temperature and pressure. The new methods developed at atmospheric pressure for predicting BD viscosity were found to have better predictive ability than those commonly used in literature. In particular, the models developed with the Lewis and Squires equation fitted to biodiesel feedstock (LSDB model) and the same equation using the predicted degree of unsaturation (DU) (LSDU1 model) presented a very good performance with average relative deviation (ARD)