In this study, Response surface methology (RSM) was used for the optimization of process variables in combination with the factorial experimental design of central composite design (CCD). RSM is a useful method for studying the effect of several variables influencing the responses by varying them concurrently and carrying out a limited number of experiments. The CCD is an effective design that is ideal for sequential experimentation and allows an equitable amount of information for testing the lack of fit while not connecting an unusually large number of design points (Özer et al 2009).The central composite design was working for determining the optimum hydrolysis condition for mannooligosaccharides copra meal hydrolysate production. The statistical software Minitab 16 Software (Minitab Inc USA) was used for design of experiments, regression and graphical analyzes of the data obtained, and statistical analysis of the model to evaluate the analysis of variance (ANOVA). Percent of enzyme concentration, Percent of substrate concentration and reaction time were chosen as three independent variables in the formulating process. Accordingly, the CCD matrixes of 20 experiments for the full design of two factors were used for building quadratic models. The experimental data attained from the CCD model experiments can be indicated in the form of the following equation:Yi = f(y) = ?0 + ?ik=1?ixi + ?ik=1?iixi2 + ?ik=1?iixi2+ ?ik=1?ik=1 + ?ijxij + ? (1)Where Yi is the predicted response used to relate to the independent variable, k is the number of independent variables (factors) Xi (i = 1, 2, 3); while ? is a constant coefficient and ?i, ?ij and ?ii the coefficient of linear, interaction and square terms respectively and ? is the residual error (Moghaddam et al 2010). The statistical combinations of variables in uncoded and actual values along with the predicted and experimental responses are presented in Table 2. Table 2 depicts a complete 23 factorial design with four center points in cube, and six axial points and two center points in axial. The number of experiments required (N) is given by the expression 2k (23 = 8; cube points) + 2k (2×3 = 6; axial points) + 6 (center points; 6 replications). For the response surface method involving CCD, a total of 20 experiments was conducted for the three factors at five levels with the replicates at the center point.Multivariate regression analysis with model equation (1) was carried out on the data using to yield equation (2) which was used to optimize the product responses.Y = ?0 + ?1×1+ ?2×2 + ?3×3 +?11×12 + ?22×22 + ?33×32 + ?12x1x2 + ?13x1x3+ ?23x2x3 + ? (2)The model developed for each purpose was then examined for significance and lack-of fit, while response surface plots were designed with the same software. The quality of the polynomial model was articulated by the coefficient of determination, namely, R2 and R2 (adj).