Multimodal size distributions can result from a mixing of two or more component distributions and arise in quite different application areas. Physical and statistical approaches are described for decomposition of a multimodal particle size distribution into a number of lognormal components. These approaches, incorporated in the Fortran computer program FitDist, use a nonlinear least-squares (NLLS) optimization, requiring initial parameter estimates. A hybrid deconvolution method has been developed. Differential Evolution (DE), is used for generation of initial parameter values, followed by an NLLS optimization to derive precise parameter values at the local optimum. The DE algorithm is required to decide on the proper number of modes to be fitted. Mathematical relationships have been derived to convert the parameter values of one multimodal lognormal moment distribution, e.g., a number distribution, to those of another moment distribution, e.g., a volume distribution. Moreover, mathematical relationships have been derived to compute mean diameters D,q (Moment-Ratio notation) from the parameters of a multimodal lognormal size distribution. Fitting a 1.5th moment distribution, being just in between a number and a volume distribution, has been introduced as an instrument to balance inaccuracies in both tails of a distribution due to sampling inaccuracies or truncation of these tails. The program fits a truncated size distribution by fitting its frequency density distribution, whereas a complete size distribution is fitted by fitting the cumulative distribution. Some guidelines are given for fitting Number, Diameter, Surface area, and Volume distributions to measured size distributions. Although fitting of multimodal normal distributions is an option, higher moment distributions will not be fitted as these distributions are not normally distributed. Practical examples demonstrate the validity of the method to decompose multimodal particle size distributions by use of DE.