Flotation process is widely used in mineral industry for the separation of valuable minerals from low-grade ore slurry. There are several parameters such as the bubble size and bubble loading that predict the efficiency of the flotation process. These parameters can be used for the control of the flotation process. There are already some techniques that can be used for online monitoring of these parameters, for example, the high-speed video imaging and a probe sensor based on electrical resistance tomography (ERT). These methods, however, suffer for some limitations. The high speed video imaging gives information only on the surface of the froth and in the previously proposed ERT based techniques the conductivity of the froth is typically modeled to be smoothly varying. However, in reality the froth is composed of different size of bubbles having highly conductive surface and very low conductive interior which configuration cannot be modeled with smoothly varying conductivity distribution. In this paper, we propose a computational approach in which the structure of the froth is modeled and both the bubble size and the conductivity of the boundary of the bubbles are estimated. The proposed approach utilizes data measured with the standard ERT probe. The estimated bubble size and conductivity of the boundary of the bubbles are compared to online measured camera based estimates of the bubble size and bubble loading. The proposed approach is evaluated with simulated measurements and real data from Pyhasalmi Mine. The results show that there is a high correlation between the camera based and the ERT based estimates of the bubble size. Furthermore, some of the parameters obtained from the ERT based method correlate well with the camera based estimate of the bubble loading. (C) 2014 Elsevier Ltd. All rights reserved.