The flotation testing of sulphide ores for flowsheet development, or for the improvement of existing flowsheets in operations, has been practiced for a century or so. This practice has evolved at both laboratory and operations scales, as a result of contributions by various workers in this field. In this review, two major contributions to improved practice are discussed, viz Process Mineralogy and representative sampling. A description of modern best practice is proposed, particularly in the context of circuit changes or reagent selection and the use of mixed collectors. Process Mineralogy has contributed significantly by way of powerful information that reveals process implications such as those resulting from grinding strategies or flotation selectivity challenges. Only recently has the best practice of sampling been connected to flotation testing. High Confidence Flotation Testing, which incorporates appropriate sampling models, was proposed in 1995, and used Gy's minimum sample mass and Safety Line models. Statistical Benchmark Surveying, a method for extracting representative suites of survey samples from an operating plant, was added in 2005. A new addition is the small. scale evaluation of floatability using the JKMSI, which enables the testing of small samples such as of drill core, and is demonstrating good agreement with operations data. Two generations of improved practice are reviewed. The first is when this practice was retrofitted to serve existing concentrators that had been conventionally designed, in a reactive approach. The second is serving new design opportunities before commissioning, where predictive value is added to the project with a more complete understanding of the process implications drawn from the sampling and characterisation of drill core. It is shown that when these connections are made and modern quality controls are applied to the flotation testing, much clearer conclusions are drawn, and tighter metal balances achieved, with better metallurgical performance. This all results in a lower level of error in the metallurgical test data, reducing project risk, offering significantly shorter project schedules, and better startup performance for the project, and also, as the results are more precise, allowing comparison of options with smaller recovery and grade gains. (C) 2014 Elsevier Ltd. All rights reserved.