Almost a half century after it was introduced, Wilks’ statistic has come into application in industrial manufacturing process variability monitoring. This is an important breakthrough in the way experts monitor the variability of manufacturing processes which is vital in modern industry. It leaves behind the traditional practice characterized by the use of sample size n which equals 1, if the process variability monitoring is based on individual observations and is greater than the number of variables p if one works with subgroup observations. The use of Wilks’ statistic allows us to work with n < p. This paper contains a review on process variability monitoring based on individual observations. First, some historical backgrounds of process variability monitoring in the general scheme was reviewed before it was revealed where the philosophy of Wilks’ statistic could be further interpreted. Subsequently it was indicated that the way to monitor the process variability depended on how the variability itself was measured. Finally, a new statistic for detecting the shift in variability based on individual observations was introduced and then a new control chart was proposed. The performance of the proposed chart as compared with Wilks chart, was quite promising. Therefore, some recommendations were given to better understand the history of manufacturing process variability.