It has been postulated that information processing in the brain is based on precise temporal correlation of neural activity across populations of neurons. In a recent study we found spatio-temporal spike patterns in experimental recordings from monkey motor cortex, and here we study if those could be explained by a synfire chain (SFC) like model. The model is composed of groups of neurons connected in feed-forward manner from one group to the next with high convergence and divergence. When activated, e.g., by a current pulse to the first group, spiking activity in the SFC is synchronous within neurons of the same group and propagates from group to group. When a few neurons from different groups are recorded from such an SFC, and the SFC is repeatedly activated, we would find a spatio-temporal spike pattern repeating across trials. Here, we take the statistics of the STPs found in the experimental data from 20 sessions as a reference to compare to a simulated network. Distributions of the data we take into account include 1) the pattern sizes, i.e. the number of neurons involved in the patterns, 2) the number of patterns a single neuron is involved in, 3) the durations of the patterns, and 4) the spatial distances of the patterns across the electrode array used to record the data. For the simulations, we embed SFC(s) in an anatomical model of the respective layer of the motor cortex, defined by its height and the density of the neurons. Model parameters are the length of the SFC, the number of neurons per group, the spatial extent of each neuronal group, and the distance between subsequent groups. Given the size and reach of the Utah array electrodes, we derive the probability of recording neurons from the SFC network. An SFC is considered detected if at least two neurons from two different groups are recorded. We find that depending on the model parameters, an embedded SFC can be detected with high probability, despite the massive subsampling of the cortex by the Utah array. Furthermore, to achieve multiple membership of a neuron in different patterns, we embed multiple SFCs that overlap. The fitting of the model to the pattern data constrains the spatial SFC parameters: the chains have to be broadly distributed in space and contain many neurons per group to match the experimental results.