The development of a new method to estimate concentrations of condensing organics (MECCO) is described. A Markov chain Monte Carlo method is applied, and by using measured particle size distribution and random vapor concentrations as input, the predicted changes in particle population by an aerosol dynamics model are utilized. The method provides the ambient vapor concentrations required for the observed particle growth in particle number size distribution data, assuming all growth can be attributed to net condensation of super-saturated vapors. In this paper, MECCO was coupled with the UHMA box-model to provide aerosol dynamics. With few changes, MECCO could be applied to study other input parameters, and coupled with other dynamics models as well. Evaluation of the method was carried out with simulated output from the UHMA model using the assumption of three organic vapors, and MECCO-UHMA was able to estimate their concentrations with great accuracy. However, the condensation of vapors is currently considered irreversible, since the used particle size distribution data do not provide information on the composition of particles. The distinguishing between the vapors is based on few vapor parameters, which limits the possibilities of identifying actual vapors. An example of atmospheric application is also presented. This revealed the importance of quality control of the input particle concentrations: instrumental noise and changes in the observed air mass pose challenges for the presented method. Data need to be smoothed in a reasonable way so that the point-like measurements can be utilized, but also so that the important information on particle growth is conserved. MECCO is a useful tool to approximate vapor concentrations and may be applied to estimate vapor properties as well. However, a computationally efficient and physically accurate aerosol dynamics model is essential for MECCO's performance.