The validity of many statistical methods, including statistical process control (SPC) rests on the assumption that the probability distribution is nearly normal. The assumption of normal distribution of variables is not critical in the construction of a confidence interval; however, it is important in constructing a tolerance interval to include a specified proportion of the population. The application of SPC to an industrial process whose variables cannot be described by a normal distribution can be a major source of error and frustration. An assumption of normal distribution for some filter performance characteristics can be unrealistic and these characteristics cannot be adequately described by a normal distribution.A transformation of data can improve the agreement with normality and can greatly extend the range of validity to statistical methods. This paper examines some transformation methods, which can be applied to non-normal distribution.Filter performance characteristics represent variables, which could be non-normal. This paper highlights some issues of non-normal distribution of filter performance data and addresses methods that make SPC applicable to non-normal distribution of efficiency data.