This paper presents the development of soft sensor models for continuous monitoring of propylene content in rafinery propane/propylene splitter. The goal of this paper is to develop soft sensor models based on given experimental data about refinery process of propane and propylene separation, validate and compare developed models. The development of models began with the preprocessing of the collected measurement data. Soft sensor models were created in the Python programming language using the methods of multilayer percepton networks and long short-term memory networks. During development of multilayer percepton networks model it was tested how hyperparameters such as number of neurons in hidden layers and activation function (sigmoid, tanh, ReLU i ELU) affect on quality of the developed neural networks. During the development of LSTM models, additional testing of the number of time steps into the past and the number of LSTM units on model quality were examined. A statistical analysis of the results was carried out, which shows that both types of models give high correlation values of model data with real propylene content data, so both types of models can be applied in the refinery information system. Error trend analysis and error histogram of the developed models additionally indicate the satisfactory quality of the developed models. By applying the developed soft sensors it is possible to decrease the need for high cost measurement equipment and equipment that breaks down frequently, and ultimately completely replace the existing analyzers in the process and improve the automatic management of the process. Application of software sensors will improve automatic process control, which will result in more stable process management at the plant and higher quality of the final product.