A neural network is configured and trained to measure the polymer crytallinity in real time and non-intrusive manner. After the training, input to the neural network becomes the laser Raman spectrum at selected ferquencies and the output from the network is the current crystallinity of polymer. In order to train the neural network, a training data set is constructed where the crystallinities corresponding to a given set of Raman spectra are pre-determined by the small angle light scattering (SALS) methodology. The technique is applied to measure the crystallinity of low-density thin polyethylene (LDPE) film. A typical sampling period for the determination of the crystallinity is around 12 s. The technique is compared to the principal component analysis that uses the same input data for calibration.