Microseismicity is expected in potash mining due to the associated rock-mass response. This phenomenon is known, but not fully understood. To assess the safety and efficiency of mining operations, producers must quantitatively discern between normal and abnormal seismic activity. In this work, statistical aspects and clustering of microseismicity from a potash mine in Saskatchewan, Canada, are analyzed and quantified. Specifically, the frequency-magnitude statistics display a rich behaviour that deviates from the standard Gutenberg-Richter scaling for small magnitudes. To model the magnitude distribution, we consider two additional models, i.e., the tapered Pareto distribution and a mixture of the tapered Pareto and Pareto distributions to fit the bi-modal catalogue data. To study the clustering aspects of the observed microseismicity, the nearest-neighbour distance (NND) method is applied. This allowed us to identify potential cluster characteristics in time, space, and magnitude domains. The results of the clustering analysis of microseismicity indicate that the majority of earthquakes can be treated as independent background events mostly driven by underground mining operations. However, there is some clustering of seismicity and the formation of limited aftershock sequences. The frequency-magnitude distribution of seismicity exhibits a relatively high b-value (b = 2.39�0.15) indicating that the seismic events are distributed in a rather narrow magnitude range above the completeness threshold. The interevent triggering is also suppressed and does not show any significant cascade-like propagation of seismicity. The implemented modelling approaches and obtained results will be used to further advance strategies and protocols for the safe and efficient operation of potash mines.