1. A method for evaluating a hydrocarbon fuel from a family of hydrocarbon fuels selected from the group consisting of gasoline, diesel fuel, kerosene, naphtha and jet fuel to determine a desired parameter selected from the group consisting of Reid vapor pressure, simulated distillation values, research octane number, motor octane number, oxygen content, specific gravity octane number, bromine number, aniline point, smoke point, and combinations thereof, comprising the steps of: (1) providing a computer configured as a neural network; (2) training the neural network so as to evaluate a hydrocarbon fuel from the family of hydrocarbon fuels to determine the desired parameter, said training comprising the steps of: (a) selecting a plurality of hydrocarbon fuels from the family of hydrocarbon fuels to be evaluated; (b) obtaining an NIR spectra for each of said plurality of hydrocarbon fuels; (c) codifying each of the NIR spectra obtained by providing a base line correction and thereafter reducing the base line corrected spectra to a desired number of points corresponding to the parameters being evaluated; (d) developing a first matrix from the desired number of points, said first matrix to be subsequently inputted to the neural network; (e) obtaining a second matrix of parameter values from an analytical evaluation of the plurality of hydrocarbon fuels; (f) processing the first matrix and the second matrix in the neural network to obtain a functional relationship between the first matrix and the second matrix so as to develop a weighted matrix; and (g) repeating steps (b) through (f) to obtain an optimal weighted matrix. (3) comparing an NIR spectra from a hydrocarbon fuel selected from the family of hydrocarbon fuels with the optimal weight matrix in the neural network to obtain a predicted value of the desired parameters for the selected hydrocarbon fuel.