In this article, an unsupervised learning method is presented with the objective of modeling, in real-time, the main operating modes (OM) of distribution transformers. This model is then used to assess the operational condition through use of two tools: the operation map and the health index. This approach allows, mainly, for a reduction in the need for the interpretation of results by specialists. The method used the concepts of k-nearest neighbors (k-NN) and Gaussian mixture model (GMM) clustering to identify and update the main OMs and characterize these through operating mode clusters (OMC). The evaluation of the method was performed using data from a case study of almost one year in duration, along with five in-service distribution transformers. The model was able to synthesize 11 magnitudes measured directly in the transformer into two latent variables using the principal component analysis technique, while preserving on average more than 86% of the information present. The operation map was able to categorize the transformer operation into previously parameterized levels (appropriate, precarious, critical) with errors below 0.26 of standard deviation. In addition, the health index opened the possibility of identifying and quantifying the main abnormal variations in the operating pattern of the transformers.