The properties and limitations of maximum likelihood (ML) inference of genome-wide mutation rates (U) and parameters of distributions of mutation effects are investigated. Mutation parameters are estimated from simulated experiments in which mutations randomly accumulate in inbred lines. ML produces more accurate estimates than the procedure of Bateman and Mukai and is more robust if the data do not conform to the model assumed. Unbiased ML estimates of the mutation effects distribution parameters can be obtained if a value for U can be assumed, but if U is estimated simultaneously with the distribution parameters, likelihood may increase monotonically as a function of U. If the distribution of mutation effects is leptokurtic, the number of mutation events per line is large, or if genotypic values are poorly estimated, only a lower limit for U, an upper limit for the mean mutation effect, and a lower limit for the kurtosis of the distribution can be given. It is argued that such lower (upper) limits are appropriate minima (maxima). Estimates of the mean mutational effect are unbiased but may convey little about the properties of the distribution if it is leptokurtic.