The industries related to the production or use of cyanide must ensure high safety standards in order to minimize the risk to personnel exposed to hydrogen cyanide (HCN). This can be reached by accomplishing a global search of all parameters involved in the process, taking also into account the local regulations for HCN emanations. In this scenario, there is a high interest in developing methods capable of quantifying the HCN generation in different processes or equipment and in that way the implementation of remediation actions in the process operations or in the design of future projects can be taken. Nowadays, the most used method to estimate HCN generation is based on the dissociation curve of free cyanide and the Henry's law constant.However, this method consistently overestimates the volatilized HCN in exit gases due to there is no inclusion of the chemical interactions of cyanide with others species (e.g., zinc, copper, and nickel), and also does not consider the mass transfer of HCN, the most influential factor on HCN volatilization. The mass transfer of HCN in the liquid phase represents the main resistance of HCN transport into the gas phase, which is dependent on system characteristics such as geometry, hydrodynamics and state conditions. With this background in mind, a phenomenological method to estimate HCN generation in any system is proposed in the current chapter. This theoretical method is mainly based on the thermodynamic equilibrium of the different cyanide complexes species present in a solution, the phase equilibrium behaviour based on the Henry's law equilibrium, and the mass transfer of HCN into the air based on a resistances-in-series model to determine the HCN content in the liquid and gas phases. In addition, the proposed method also evaluates the effect of system conditions (temperature, pressure, and pH) and the operation characteristics or equipment used. Thus, this phenomenological method can be a powerful and valuable tool for quantifying HCN generation in realistic simulations, along with providing rapid identification of risk conditions in the process and more criteria for investment project decision making. © 2013 Nova Science Publishers, Inc.