Simple SummaryArtificial insemination with extended liquid boar semen is widely used in the swine industry. The identification of the relationship between boar sperm characteristics and fertility could be of substantial importance to reproduction management. This study evaluated the relationship between boar sperm characteristics and sperm/seminal plasma proteins with main parameters of field fertility. Immotile spermatozoa and spermatozoa with biochemically active plasma membranes affected the number of live-born piglets and litter size of ≥12 piglets. The proteins osteopontin 70 and glutathione peroxidase 5, both separately and in combination, affected the farrowing rate. The combination of immotile sperm and protein osteopontin 70 explained the variation regarding litter size with ≥12 piglets. In conclusion, the evaluation of semen quality variables combined with the evaluation of specific sperm or seminal plasma proteins could provide useful information on in vivo fertilizing capacity of semen doses.This study aimed to evaluate boar sperm characteristics and proteins, in relation to their importance regarding in vivo fertility. Sixty-five ejaculates were used and 468 sows (parity ≥ 2) were inseminated. Sperm CASA kinetics, morphology, viability, DNA fragmentation, mitochondrial membrane potential, sperm membrane biochemical activity (HOST) and sperm proteins (Heat Shock Protein 90-HSP90, glutathione peroxidase-5-GPX5, Osteopontin 70-OPN70) were assessed and related to field fertility (number of live-born piglets—NLBP, litter size ≥ 12 piglets—LS, farrowing rate—FR). Statistical analysis was conducted with simple and multiple regression models. Simple regression analysis showed that immotile sperm (IM) significantly affected the NLBP and LS, explaining 6.7% and 6.5% of their variation, respectively. The HOST positive spermatozoa significantly affected the NLBP and LS, explaining 24.5% and 7.8% of their variation, respectively. Similarly, sperm with activated mitochondria significantly affected the NLBP, explaining 13.5% of its variation. Moreover, the OPN70 affected LS and FR, explaining 7.5% and 10.8% of their variation, respectively. Sperm GPX5 protein affected FR, explaining 6.7% of its variation. Multiple regression analysis showed that the combination of IM and/OPN70 explains 13.0% of the variation regarding LS, and the combination of GPX5 and OPN70 explains 13.6% of the variation regarding FR. In conclusion, the estimation of parameters IM, membrane biochemical activity, mitochondrial membrane potential, OPN and GPX5 can provide useful information regarding semen doses for field fertility.