Sympathetic nervous flow to the vasculature plays a critical role in control of regional blood flow; however, traditional processing methods of multifiber recordings cannot reliably discriminate physiologically irrelevant information from actual nerve activity, and alternative wavelet methods suffer from subjectivity and lack of a well-specified model. We propose an algorithm that allows objective threshold selection under general assumptions regarding the sparsity and statistical structure of the neural signal and noise. Our study shows that the conditional expectation of the actual nerve signal can be estimated and used to maximize the signal-to-noise ratio (SNR). We evaluated the algorithm's performance on artificial datasets and on actual multifiber recordings (44 datasets from 22 subjects, and 1 set from a rat). On artificial datasets, the algorithm identified 70% and 80% of the spikes at -3.5 and 0.5 dB SNR with a good match between the actual and estimated spike count (R2 = 0.719, p < 0.001). On actual recordings, the overall improvement in performance compared to that of a traditional processing method was significant (t = 3.88; p = 0.0002). Our results show the applicability of this algorithm to multifiber recordings not only in humans, but also in other species.