As a neural approach, fuzzy ARTMAP has the capability to resolve the stability-plasticity dilemma, i.e., incorporate novel information but preserve significant past learning. In this paper, we propose two non-parametric algorithms for the fuzzy ARTMAP to provide soft classification outputs. These algorithms, which are triggering-frequency-based and are called ART Commitment (ART-C) and ART Typicality (ART-T), expressing in the first case the degree of commitment the classifier has for each class for a specific pixel and in the second case, how typical that pixel's reflectances are of the ones upon which the classifier was trained for each class. To evaluate the two proposed algorithms, soft classifications of a SPOT HRV image were undertaken. A Bayesian posterior probability soft classifier, Mahalanobis typicality soft classifier, SOM Commitment and SOM Typicality measures were also used as a comparison. Principal Components Analysis (PCA) was used to explore the relationship between these measures. Results indicate that similarities exist among the ART-C, SOM-C and a parametric Bayesian posterior probability classifier, and among ART-T, SOM-T and a Mahalanobis typicality classifier. Additionally, ART models distinguish themselves from all other four models due to its special properties.