A neural network is trained to estimate the unknown crystallinity and temperature of Nylon 6 and its nanocomposites while the material is undergoing cooling at a fixed rate. The innovation of the work is that the full spectrum captured by the laser Raman spectroscope is used to train a neural network for estimation of crystallinity and temperature. The small-angle light scattering (SALS) and differential scanning calorimetry (DSC) data were used to provide the training examples for the neural network. Results indicate that the neural network can provide reliable estimates of the crystallinity and temperature provided that there is a sufficient number of training data available. Neural network methodology is also efficient in establishing the crystallization-temperature relationship as a function of cooling rate and demonstrates the heterogeneous nucleation effect of nanoclay in the nylon 6 matrix. (C) 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 474-483, 2004.