Many procedures have been developed to assist in the early diagnosis of breast cancer. In this context, ultrasound has become an indispensable tool to distinguish benign and malignant lesions. Due to the subjectivity on interpreting images, CAD schemes have provided to the specialist a second opinion more accurate and reliable. Thus, this research presents a methodology for the automatic detection and characterization of breast sonographic findings. We performed the tests in ultrasound images obtained from breast phantoms made of tissue mimicking material. When the results were considerable, we applied the same techniques to clinical examinations. The process was started employing of a preprocessing (wiener filter, equalization and median filter) to minimize noise. Then, five segmentation techniques were investigated to determine the most concise representation of the lesion contour, enabling to consider the neural network SOM the most relevant. After the delimitation of the object, the most expressive features were defined to the morphological description of the finding, generating the input data to the neural Multilayer Perceptron (MLP) classifier. The accuracy achieved during training with simulated images was 94.2%, producing an AUC of 0.92. To evaluating the data generalization, the classification was performed with a group of unknown images to the system, both to simulators as to clinical trials, resulting in an accuracy of 90% and 81%, respectively. The proposed classifier proved to be an important tool for the diagnosis in ultrasonography breast.