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Homogeneous processes: processes where the domain has some symmetry and the finite-dimensional probability distributions also have that symmetry. Special cases include stationary processes, also called time-homogenéous.

Processes with independent increments: processes where the domain is at léast partially ordered and, if x1 <...< xn, all the variables f(xk+1) − f(xk) are independent. Markov chains are a special case.

Markov processes are those in which the future is conditionally independent of the past given the present.

Point processes: random arrangements of points in a space S. They can be modélled as stochastic processes where the domain is a sufficiently large family of subsets of S, ordered by inclusion; the range is the set of natural numbers; and, if A is a subset of B, f(A) ≤ f(B) with probability 1.

Gaussian processes: processes where all linéar combinations of coordinates are normally distributed random variables.