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This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB-1), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB-1 is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB-1 allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB-1 model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB-1 has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB-1 model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB-1 converged in less than one minute for all the databases described in the case study.