Two methods for generating smoothing splines are compared and applied to data from a fed-batch fermentation process. One method chose both the degree of the spline and its parameters by minimizing the generalized cross validation (GCV) function using a genetic algorithm (GA). The other method adjusted the smoothing spline to a specified chi-square goodness-of-fit, requiring prior knowledge of the measurement variability. The GCV/GA method led to excellent results with all the fermentation data records. The goodness-of-fit method gave a family of spline fits; splines with a low percentage fit extracted trends from the data, while for general use a 50% fit appeared satisfactory. The goodness-of-fit method executed more quickly than the GCV/GA method, but the GCV/GA method was more generally applicable as it chose both the degree of the spline and the amount of smoothing automatically.