In discussion of process capability indices for normal and non-normal processes with symmetrical and asymmetrical tolerances it is noted that pertinent research work are based on either proposing new capability indices to purport better properties in certain circumstances or replacing existing by other procedures. In this paper an application is presented to estimate capability indices under non-normality using Johnson system for an earlier used data set. Numerous work have been published in literature to estimate these indices using Johnson system. This paper present an application of new learning approach to translate the measurements of non-normal process to Johnson distributions countenanced the user to check foremost assumptions of estimating capability indices. In this approach the exact percentage points (0.135 lower and upper) are obtained acquiring the knowledge of density function before estimating capability indices under non-normality. Earlier these points are estimated from the process measurements come from non-normal process without knowledge of the density function. The procedure is illustrated by a data set which is transformed in SB Johnson distribution and the percentage points are obtained from the proposed modified procedure results better while compare with existing procedure. The route is explained by flow chart and program is made in R-console.