diff --git a/Project.toml b/Project.toml
index 775a50c..78d2aca 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "AdvancedMH"
 uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170"
-version = "0.6.5"
+version = "0.6.6"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
diff --git a/README.md b/README.md
index 6f05a18..1dece12 100644
--- a/README.md
+++ b/README.md
@@ -111,14 +111,19 @@ rw_prop = RandomWalkProposal(Normal(0,1))
 Different methods are easily composeable. One parameter can be static and another can be a random walk,
 each of which may be drawn from separate distributions.
 
-## Multithreaded sampling
+## Multiple chains
 
-AdvancedMH.jl implements the interface of [AbstractMCMC](https://github.com/TuringLang/AbstractMCMC.jl/), which means you get multiple chain sampling
-in parallel for free:
+AdvancedMH.jl implements the interface of [AbstractMCMC](https://github.com/TuringLang/AbstractMCMC.jl/) which means sampling of multiple chains is supported for free:
 
 ```julia
-# Sample 4 chains from the posterior.
-chain = psample(model, RWMH(init_params), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
+# Sample 4 chains from the posterior serially, without thread or process parallelism.
+chain = sample(model, RWMH(init_params), MCMCSerial(), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
+
+# Sample 4 chains from the posterior using multiple threads.
+chain = sample(model, RWMH(init_params), MCMCThreads(), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
+
+# Sample 4 chains from the posterior using multiple processes.
+chain = sample(model, RWMH(init_params), MCMCDistributed(), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
 ```
 
 ## Metropolis-adjusted Langevin algorithm (MALA)
diff --git a/src/AdvancedMH.jl b/src/AdvancedMH.jl
index dadc411..613472b 100644
--- a/src/AdvancedMH.jl
+++ b/src/AdvancedMH.jl
@@ -22,7 +22,7 @@ export
     MALA
 
 # Reexports
-export sample, MCMCThreads, MCMCDistributed
+export sample, MCMCThreads, MCMCDistributed, MCMCSerial
 
 # Abstract type for MH-style samplers. Needs better name? 
 abstract type MHSampler <: AbstractMCMC.AbstractSampler end