A method that combines experimental airfoil coefficient data with numerical data has been developed to construct airfoil performance tables given limited data sets. This work addresses the problem faced by engineers and aerodynamicists that currently rely on incomplete performance tables when researching airfoil characteristics. The method developed utilizes the Sequential Function Approximation (SFA) neural network tool and employs a simple regularization scheme to fuse multi-dimensional experimental and computational fluid dynamics (CFD) data efficiently. The method is considered an adaptive and robust tool requiring relatively little computational demand and minimal user dependence. An existing performance table for the NACA 0012 airfoil was used as a test case to verify the feasibility of the SFA-fused network. A second test case assesses the method's viability for a more realistic and challenging problem using highly sparse and scattered data sets for the SC1095 airfoil. Results from both studies realize the method's capability to make consistent approximations and smooth interpolations given only limited experimental data. Comparisons are made with other scattered data approximation techniques. The testing conditions, requirements, and limitations of this approach are discussed and future applications and recommendations are made.