Speech-language assessment of stuttering is traditionally manual, subjective, and time-consuming. This paper presents the development of software for automatic detection and classification of stuttering-related disfluencies in Brazilian Portuguese, aiming to support clinical assessment. The system follows a two-stage hybrid approach. In the first stage, it applies deterministic algorithms based on automatic speech recognition (ASR) and temporal information to identify simple disfluencies, such as repetitions and pauses. In the second stage, it employs a hierarchical architecture combining a Kohonen network (Self-Organizing Map, SOM) and a Multilayer Perceptron (MLP) to classify complex disfluencies, specifically blocks and prolongations, using acoustic features. Because no publicly available annotated resources exist for this task in Brazilian Portuguese, we built a initial dataset annotated by specialists. The system achieved 89.5% accuracy in classifying complex disfluencies, with a Matthews Correlation Coeficient (MCC) of 0.812. These results indicate the feasibility of the tool as decision support for clinical assessment and establish a baseline for future research.