Purpose. This guide introduces the parameterization and practical use of Spectral Fault Receptive Fields (SFRFs) within the MATLAB toolbox (version 1.1.x), emphasizing their role as a structured framework for condition indicator (CI) construction. Approach. The guide demonstrates the SFRFs MATLAB toolbox, covering the workflow from installation to the definition of customized receptive field responses. It compares SFRF-based responses with standard condition indicators and illustrates how the new parameterizations enable the injection of custom Receptive Field Response Functions (RFRFs), allowing classical spectral descriptors to be embedded within the harmonic lattices induced by characteristic fault frequencies. The methodology is illustrated on run-to-failure data from the XJTU-SY open dataset. Key insights. Receptive Field Response Functions (RFRFs) provide sufficient flexibility to construct perceptual channels with varying sensitivity to degradation phenomena. SFRF-based condition indicators do not replace standard indicators; instead, they organize them into controllable perception channels. Effective characterization benefits from a multi-channel perspective, reflecting trade-offs between sensitivity, smoothness, and interpretability. Implementation. The toolbox supports flexible experimentation through behavior injection and practical deployment through logging and parallelization, enabling integration into PHM workflows. Contribution. The SFRF framework provides a structured mechanism for selectively harnessing spectral information through parameterized receptive fields, supporting fault-oriented analysis of degradation in rotating machinery. Funding. This work was carried out within the framework of the ARCHIMEDES project, supported by the Chips Joint Undertaking under Grant Agreement No. 101112295, with co-funding from National Authorities. Additional support was provided by the Austrian Research Promotion Agency (FFG) under Grant Agreement No. FO999899377.