Several measurement modalities have been developed over the years for various nondestructive testing and evaluation (NDT&E) applications, such as ultrasonic, magnetic flux leakage, and eddy current testing, all of which have been used extensively in pipeline defect identification. While it is generally believed that different testing modalities provide complementary information, only a single testing modality is typically used for a given application. This is in part due to lack of effective, computationally feasible data fusion algorithms that are applicable to NDT&E signals. Such an algorithm capable of data fusion can combine information from two or more different sources of data, giving more insight and confidence to the data analysis than a decision that would otherwise be based on either of the sources alone. Learn++, previously introduced as an incremental learning algorithm, was applied to a NDT&E data fusion application. Specifically, we generated two ensembles of classifiers, one trained on ultrasonic signals, and the other on corresponding magnetic flux leakage signals obtained from stainless steal samples that contained five classes of discontinuities: crack, pitting, weld, mechanical damage, and no discontinuity. We have observed that the prediction ability of the automated classification system, as measured by the accuracy and reliability of the classification performance on validation data, was significantly improved when the two data sources were combined using Learn++.