This document proposes an approach for interactively smoothing experimental data using a Savitzky-Golay (S-G) filter. An optimization problem is formulated to minimize two criteria: total absolute error and integral smoothness. Pareto optimal solutions are determined for the filter parameters of polynomial degree and number of supporting points. The μ-selection method is used to find subsets of Pareto optimal solutions ranked by compromised efficiency. The highest ranked solution is the Salukvadze optimum that balances the criteria most evenly. The approach allows choosing S-G filter parameters that adequately represent and smooth the data. An example applies this to smooth measured acceleration data from a vibrating mechanism.