Soft sensor application for properties estimation of splitter bottom product in a crude distillation unit (CDU) is investigated. Based on continuous temperature, pressure, and flow measurements, two soft sensors are developed as estimators of the initial boiling point and end boiling point of splitter product. Soft sensor models are developed using multiple regression techniques and neural networks. After performing multiple linear regression analysis, it was concluded that linear models are not sufficiently accurate for the implementation in the real plant. Within multi-layer perceptron (MLP) and radial basis function (RBF) neural networks, different learning algorithms are used (back propagation with variations of learning rate and momentum, conjugate gradient descent, Levenberg-Marquardt) as well as pruning and Weigend regularization techniques. Statistics and sensitivity analysis are provided for both models. Two developed soft sensors will be used as on-line estimators of heavy naphtha properties and for control purposes.