Native forests are subject to variations in the structure of the different strata. These modifications can have a natural origin due to phenomena such as climate variation, fires, or floods; or they can be anthropic, due to the unsustainable use of forest resources. In general, according to the dominance of the different strata, the forests are classified as virgin, successional, or renoval. In the realization of inventories, there are situations in which the operator can offer a doubtful classification with a high degree of subjectivity. The purpose of this work has been to provide to technicians an objective tool for the classification of successional status in native forests. In this work, it has been found that there is a relationship between the presence of combinations of different plant species, both arboreal and shrub, related to the classification of forests. This relationship has been analyzed through the application of neural networks in two steps: First, a perceptron was applied, then a probabilistic neural network. The analysis through an artificial neuronal network of these two stages has allowed us to develop equations that through the presence/absence of 16 species allows to classify objectively the successional state. This analysis demonstrated an agreement of 83% with the subjective classification of trained field assessors.