We show that numerical inversion of the Laplace transform by using the maximum entropy method can be successfully applied to the analysis of complex kinetic processes involving chemical and folding-unfolding changes in proteins. First, we present analyses of simulated data which support that: (i) the maximum entropy calculation of rate distributions, combined with Monte Carlo analyses of the associated uncertainties, yields results consistent with the information actually supplied by the data, thus preventing their over-interpretation; (ii) maximum entropy analysis may be used to extract discrete rates (corresponding to individual exponential contributions), as well as broad rate distributions (provided, of course, that the adequate information is supplied by the data). We further illustrate the applicability of the maximum entropy analysis with experimental data corresponding to two nontrivial model processes: (a) the kinetics of chemical modification of sulfhydryl groups in glycogen synthase by reaction with Ellman's reagent; (b) the kinetics of folding of ribonuclease a under strongly folding conditions, as monitored by fluorescence and optical absorption. Finally, we discuss that the maximum entropy approach should be particularly useful in studies on protein folding kinetics, which generally involve the comparison between several complex kinetic profiles obtained by using different physical probes. Thus, protein folding kinetics is usually interpreted in terms of kinetic mechanisms involving a comparatively small number of kinetic steps between well-defined protein states. According to this picture, rate distributions derived from experimental kinetic profiles by maximum entropy analysis are expected to show a small number of comparatively narrow peaks, from which we can determine, without a priori assumptions, the number of exponential contributions required to describe each experimental kinetic profile (the number of peaks), together with their amplitudes (from the peak areas), time constant values (from the peak positions), and associated Monte Carlo uncertainties. On the other hand, recent theoretical studies describe protein folding kinetics in terms of the protein energy landscape (the multidimensional surface of energy versus conformational degrees of freedom), emphasize the difficulty in defining a single reaction coordinate for folding, and point out that individual chains may fold by multiple pathways. This indicates that, in some cases at least, folding kinetics might have to be described in terms of broad rate distributions (rather than in terms of a small number of discrete exponential contributions related to kinetic steps between well-defined protein states). We suggest that the maximum entropy procedures described in this work may provide a method to detect this situation and to derive such broad rate distributions from experimental data.