This paper introduces new similarity classifiers using the Heronian mean, and the generalized Heronian mean operators. We examine the use of these operators at the aggregation step within the similarity classifier. The similarity classifier was earlier studied with other operators, in particular with an arithmetic mean, generalized mean, OWA operators, and many more. The two classifiers here are tested on four real world data sets, i.e., echocardiogram, fertility, horse-colic, and lung cancer. Three previously studied similarity classifiers are used as benchmarks to the new approaches. We observe that the similarity classifier with a generalized Heronian mean produces good classification results for the tested data sets, and is therefore more suitable for use in these classification problems.