A method is presented for inferring both the size distribution and the complex refractive index of atmospheric particulates from combined bistatic-monostatic lidar and solar radiometer observations. The basic input measurements are spectral optical depths at several visible and near-infrared wavelengths as obtained with a solar radiometer and backscatter and angular scatter coefficients as obtained from a bistatic-monostatic lidar. The spectral optical depth measurements obtained from the radiometer are mathematically inverted to infer a columnar particulate size distribution. Advantage is taken of the fact that the shape of the size distribution obtained by inverting the particulate optical depth is relatively insensitive to the particle refractive index assumed in the inversion. Bistatic-monostatic angular scatter and backscatter lidar data are then processed to extract an optimum value for the particle refractive index subject to the constraint that the shape of the particulate size distribution be the same as that inferred from the solar radiometer data. Specifically, the scattering parameters obtained from the bistatic-monostatic lidar data are compared with corresponding theoretical computations made for various assumed refractive index values. That value which yields best agreement, in a weighted least squares sense, is selected as the optimal refractive index values. That value which yields best agreement, in a weighted least squares sense, is selected as the optimal refractive index estimate. The results of this procedure applied to a set of simulated measurements as well as to measurements collected on two separated days are presented and discussed.