Prosthetic devices can be controlled using signals recorded in parts of the body where sensation and/or voluntary movement have been retained. Although neural prosthetic applications have used single-channel recordings, multiple-channel recordings could provide a significant increase in useable control signals. Multiple control signals can be acquired from recordings of a single implant by using a multi-contact electrode placed over a multi-fasciculated peripheral nerve. These recordings can be separated to recover the individual fascicular signals. Blind source separation (BSS) algorithms have been developed to extract independent source signals from recordings of their mixtures. The hypothesis that BSS algorithms can recover individual fascicular signals from nerve cuff recordings at physiological signal-to-noise ratio (SNR ~ 3–10 dB) was investigated in this study using a finite-element model (FEM) of a beagle hypoglossal nerve with a flattening interface nerve electrode (FINE). Known statistical properties of fascicular signals were used to generate a set of four sources from which the neural signals recorded at the surface of the nerve with a multi-contact FINE were simulated. Independent component analysis (ICA) was then implemented for BSS of the simulated recordings. A novel post-ICA processing algorithm was developed to solve ICA's inherent permutation ambiguities. The similarity between the estimated and original fascicular signals was quantified by calculating their correlation coefficients. The mean values of the correlation coefficients calculated were higher than 0.95 (n = 50). The effects of the geometric layout of the FINE electrode and noise on the separation algorithm were also investigated. The results show that four distinct overlapping fascicular source signals can be simultaneously recovered from neural recordings obtained using a FINE with five or more contacts at SNR levels higher than 8 dB making them available for use as control signals.