The operation of industrial flotation columns requires the control of at least two variables, the interface position and the bias rate, by manipulation of some appropriate operating variables. Problems arise due to the reliability of existing methods of measuring the bias (i.e bias = tailings water - feed water), a situation which has often forced the industry to disregard this control loop. Moreover, when using such a measuring approach, the identification of the process dynamics is impossible. A second problem arises from the possible interaction between both control loops that might call for the use of a more complex multivariable control strategy. Recent work done at Laval University has demonstrated the feasibility of an independent sensor for bias, which models the relation between the conductivity profile across the interface and the bias value using a neural network algorithm. A 250 cm height, 5.25 cm diameter Plexiglas laboratory column was equipped with a series of conductivity electrodes in its uppermost part (across the interface) to measure both interface position and bias rate. Using such equipment, the flotation column dynamics was identified. The results thus obtained permitted the design and implementation of a distributed PI control strategy, where bias was associated to wash water rate and froth depth to tails rate. Both PI controllers were tuned using a frequency-response tuning method. Results of both identification and process control are presented and discussed.