A method for predicting a parameter of a bitumen having a penetration nP at 25°C, said bitumen being obtained by means of a process applied to a hydrocarbon and/or a blend of hydrocarbons, comprising the following operative phases: a) obtaining, through experimental analyses and/or databases and/or blending models, the values of N physico-chemical parameters of the hydrocarbon or blend of hydrocarbons; a1) preparing a neural network and training it in order to obtain a trained neural network, according to the following operative phases: i) obtaining by means of experimental analyses and/or databases and/or blending models, the values of N' physico-chemical parameters of a number I of hydrocarbons and/or a number MI of hydrocarbon blends; ii) obtaining by means of experimental analyses the penetration nP values of each bitumen resulting from the application of the process P to the I hydrocarbons and/or MI blends of hydrocarbons; iii) obtaining by means of experimental analyses the value of the parameter to be predicted for each bitumen resulting from the application of the process P to I hydrocarbons and/or MI blends of hydrocarbons; iv) constructing an input matrix relating to the I hydrocarbons and/or MI blends of hydrocarbons comprising the values obtained in phases i) to iii), v) training the neural network with the input matrix obtained in phase iv) to obtain a correlation between the physico-chemical parameters of the I hydrocarbons and/or MI blends of hydrocarbons and the nP values of the bitumens resulting from the application of the process P to the I hydrocarbons and/or MI blends of hydrocarbons, and the parameter values to be predicted, thus obtaining a trained neural network. b) feeding the values of the N parameters obtained in step a) together with the nP value of the bitumen, to the trained neural network; c) predicting the parameter of the bitumen on the basis of the result of the correlation obtained by the neural network during step b). a1) preparing a neural network and training it in order to obtain a trained neural network, according to the following operative phases: i) obtaining by means of experimental analyses and/or databases and/or blending models, the values of N' physico-chemical parameters of a number I of hydrocarbons and/or a number MI of hydrocarbon blends; ii) obtaining by means of experimental analyses the penetration nP values of each bitumen resulting from the application of the process P to the I hydrocarbons and/or MI blends of hydrocarbons; iii) obtaining by means of experimental analyses the value of the parameter to be predicted for each bitumen resulting from the application of the process P to I hydrocarbons and/or MI blends of hydrocarbons; iv) constructing an input matrix relating to the I hydrocarbons and/or MI blends of hydrocarbons comprising the values obtained in phases i) to iii), v) training the neural network with the input matrix obtained in phase iv) to obtain a correlation between the physico-chemical parameters of the I hydrocarbons and/or MI blends of hydrocarbons and the nP values of the bitumens resulting from the application of the process P to the I hydrocarbons and/or MI blends of hydrocarbons, and the parameter values to be predicted, thus obtaining a trained neural network. i) obtaining by means of experimental analyses and/or databases and/or blending models, the values of N' physico-chemical parameters of a number I of hydrocarbons and/or a number MI of hydrocarbon blends; ii) obtaining by means of experimental analyses the penetration nP values of each bitumen resulting from the application of the process P to the I hydrocarbons and/or MI blends of hydrocarbons; iii) obtaining by means of experimental analyses the value of the parameter to be predicted for each bitumen resulting from the application of the process P to I hydrocarbons and/or MI blends of hydrocarbons; iv) constructing an input matrix relating to the I hydrocarbons and/or MI blends of hydrocarbons comprising the values obtained in phases i) to iii), v) training the neural network with the input matrix obtained in phase iv) to obtain a correlation between the physico-chemical parameters of the I hydrocarbons and/or MI blends of hydrocarbons and the nP values of the bitumens resulting from the application of the process P to the I hydrocarbons and/or MI blends of hydrocarbons, and the parameter values to be predicted, thus obtaining a trained neural network. b) feeding the values of the N parameters obtained in step a) together with the nP value of the bitumen, to the trained neural network; c) predicting the parameter of the bitumen on the basis of the result of the correlation obtained by the neural network during step b).