This study aims to detect the size and attribute of particles simultaneously by extracting nonlinear eigenvector of size and attribute in light scattering signals and using general regression neural network (GRNN).The scattering signals are decomposed by the method of empirical mode decomposition (EMD), and the three-dimensional energy distribution is extracted. Sample entropies of three kinds of particles with same attribute and different sizes are calculated. It is found that the sample entropy can identify the attribute of particles. In order to eliminate the influence of particle size and attribute on the scattering, the Hilbert transform is used for the light scattering signals, and time-frequency domain eigenvectors are extracted, which form a high-dimensional eigenvectors set together with the sample entropy. The eigenvectors set is summed up into six eigenvectors by the local linear embedding (LLE) algorithm and used as the input layer of the GRNN to identify the particle size and attribute. Finally, an experiment is conducted to test the 0.11 μm SiO2 particles, 2 μm and 4 μm polystyrene pellets. The results show that the accuracy of particle size detection and attribute recognition exceeds 90%.