We explore the long-term performance and stability of seven percutaneous Utah Slanted Electrode Arrays (USEAs) and intramuscular recording leads (iEMGs) implanted chronically in the residual arm nerves and muscles of three human participants as a means to permanently restore sensorimotor function after transradial amputations. We quantify the number of functional recording and functional stimulating electrodes over time. We also calculate the signal-to-noise ratio (SNR) of USEA and iEMG recordings and quantify the stimulation current necessary to evoke detectable sensory percepts. Furthermore, we quantify the consistency of the sensory modality, receptive field location, and receptive field size of USEA-evoked percepts. In the most recent subject, involving USEAs with technical improvements, neural recordings persisted for 502 d (entire implant duration) and the number of functional recording electrodes for one USEA increased over time. However, for six out of seven USEAs across the three participants, the number of functional recording electrodes decreased within the first 2 months after implantation. The SNR of neural recordings and electromyographic recordings stayed relatively consistent over time. Sensory percepts were consistently evoked over the span of 14 months, were not significantly different in size, and highlighted the nerves' fascicular organization. The percentage of percepts with consistent modality or consistent receptive field location between sessions (∼ 1 month apart) varied between 0%–86.2% and 9.1%–100%, respectively. Stimulation thresholds and electrode impedances increased initially but then remained relatively stable over time. This work demonstrates improved performance of USEAs, and provides a basis for comparing the longevity and stability of USEAs to that of other neural interfaces. USEAs provide a rich repertoire of neural recordings and sensory percepts. Although their performance still generally declines over time, functionality can persist long-term. Future work should leverage the results presented here to further improve USEA design or to develop adaptive algorithms that can maintain a high level of performance.