Increasingly complex models of the neck neuromusculature need detailed muscle and kinematic data for proper validation. The goal of this study was to measure the electromyographic activity of superficial and deep neck muscles during tasks involving isometric, voluntary, and reflexively evoked contractions of the neck muscles. Three male subjects had electromyographic (EMG) fine wires inserted into the left sternocleidomastoid, levator scapulae, trapezius, splenius capitis, semispinalis capitis, semispinalis cervicis, and multifidus muscles. Surface electrodes were placed over the left sternohyoid muscle. Subjects then performed: (i) maximal voluntary contractions (MVCs) in the eight directions ( intervals) from the neutral posture; (ii) isometric contractions with a slow sweep of the force direction through ; (iii) voluntary oscillatory head movements in flexion and extension; and (iv) initially relaxed reflex muscle activations to a forward acceleration while seated on a sled. Isometric contractions were performed against an overhead load cell and movement dynamics were measured using six-axis accelerometry on the head and torso. In all three subjects, the two anterior neck muscles had similar preferred activation directions and acted synergistically in both dynamic tasks. With the exception of splenius capitis, the posterior and posterolateral neck muscles also showed consistent activation directions and acted synergistically during the voluntary motions, but not during the sled perturbations. These findings suggest that the common numerical-modeling assumption that all anterior muscles act synergistically as flexors is reasonable, but that the related assumption that all posterior muscles act synergistically as extensors is not. Despite the small number of subjects, the data presented here can be used to inform and validate a neck model at three levels of increasing neuromuscular–kinematic complexity: muscles generating forces with no movement, muscles generating forces and causing movement, and muscles generating forces in response to induced movement. These increasingly complex data sets will allow researchers to incrementally tune their neck models’ muscle geometry, physiology, and feedforward/feedback neuromechanics.